Computational Intelligence in Time Series Forecasting pdf epub mobi txt 电子书 下载 2026
出版者:Springer
作者:Ajoy K. Palit
出品人:
页数:372
译者:
出版时间:2005-10-18
价格:USD 169.00
装帧:Hardcover
isbn号码:9781852339487
丛书系列:
图书标签:
- 计算
- forcasting
- Time Series Forecasting
- Computational Intelligence
- Machine Learning
- Neural Networks
- Forecasting Models
- Data Analysis
- Artificial Intelligence
- Pattern Recognition
- Time Series
- Algorithms
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具体描述
《计算智能在时间序列预测中的应用》 本书深入探讨了计算智能技术在时间序列预测领域的核心作用及其前沿应用。时间序列数据，如股票价格、天气模式、经济指标以及传感器读数，构成了我们理解和预测未来趋势的基础。然而，这些数据往往伴随着复杂性、非线性和潜在的噪声，使得传统的预测方法在面对这些挑战时显得力不从心。计算智能，作为一种模仿生物和自然系统行为的计算范式，为我们提供了一系列强大而灵活的工具，以应对这些复杂性。 本书的重点在于阐述如何运用诸如人工神经网络（ANNs）、模糊逻辑（Fuzzy Logic）、进化计算（Evolutionary Computation）以及它们之间的混合模型（Hybrid Models）来构建高效的时间序列预测系统。我们将从基础概念入手，详细介绍这些计算智能技术的工作原理、数学模型以及它们在时间序列预测任务中的具体实现方式。 人工神经网络在时间序列预测中的应用部分，我们将聚焦于循环神经网络（RNNs）及其变种，特别是长短期记忆网络（LSTM）和门控循环单元（GRU）。这些网络因其能够捕获序列数据中的长期依赖关系而成为时间序列分析的基石。读者将学习到如何构建、训练和优化这些网络模型，以解决诸如短期和长期趋势预测、季节性模式识别以及异常值检测等典型问题。此外，我们还会探讨卷积神经网络（CNNs）在从时间序列数据中提取局部特征方面的潜力，以及注意力机制（Attention Mechanisms）如何进一步增强模型的预测能力，使其能够更准确地关注序列中的关键信息。 模糊逻辑在时间序列预测中的作用部分，将介绍模糊逻辑如何处理不确定性和模糊信息，这在许多实际时间序列数据中是普遍存在的。我们将讲解模糊集（Fuzzy Sets）、模糊规则（Fuzzy Rules）以及模糊推理系统（Fuzzy Inference Systems）的设计，并展示如何将这些概念应用于构建模糊时间序列模型。这些模型能够通过人类可理解的语言表达来描述时间序列的行为，从而提供更具解释性的预测结果。本书还将介绍如何将模糊逻辑与神经网络相结合，形成模糊神经网络（Fuzzy Neural Networks），以期结合两者的优势，提升预测的鲁棒性和准确性。 进化计算在时间序列预测中的角色部分，我们将深入研究遗传算法（Genetic Algorithms, GAs）、粒子群优化（Particle Swarm Optimization, PSO）以及差分进化（Differential Evolution, DE）等算法。这些算法模仿自然选择和群体智能，能够有效地搜索复杂的参数空间，从而优化预测模型的结构、参数以及特征选择。读者将了解到如何利用进化计算来设计有效的特征提取方法，以及如何动态地调整预测模型的参数以适应不断变化的时间序列特征。 混合模型的构建与优势部分，是本书的一大亮点。我们强调将不同的计算智能技术进行有机结合，以克服单一方法的局限性，并发挥协同效应。例如，将模糊逻辑与神经网络结合，利用模糊逻辑处理不确定性，利用神经网络学习复杂的映射关系；或者将进化计算用于优化神经网络的结构和参数，从而获得更优的预测性能。本书将提供详细的案例研究，展示这些混合模型在不同时间序列预测场景下的实际应用效果，包括金融市场预测、能源负荷预测、医疗数据分析等。 此外，本书还将涵盖数据预处理与后处理技术，包括缺失值处理、数据平滑、降噪以及特征工程等关键步骤，这些步骤对于任何预测任务的成功都至关重要。我们将讨论如何有效地评估预测模型的性能，介绍常用的评估指标，并提供关于模型选择和验证的指导。 本书的目标读者是来自计算机科学、工程学、统计学、金融学以及其他对时间序列分析和预测感兴趣的专业人士和学生。通过对本书的学习，读者将能够： 深入理解计算智能技术的核心原理及其在时间序列预测中的应用。 掌握构建和优化基于人工神经网络（尤其是LSTM和GRU）的时间序列预测模型。 学会利用模糊逻辑和进化计算来处理不确定性和优化预测过程。 了解如何设计和实现有效的混合计算智能模型，以解决复杂的预测问题。 能够将所学知识应用于实际的时间序列预测任务，并评估模型的性能。 《计算智能在时间序列预测中的应用》将为读者提供一个全面而深入的视角，赋能他们在不断变化的世界中进行更准确、更智能的预测。
作者简介
目录信息
Part I Introduction
1 Computational Intelligence: An Introduction................................................3
1.1 Introduction..............................................................................................3
1.2 Soft Computing.........................................................................................3
1.3 Probabilistic Reasoning............................................................................4
1.4 Evolutionary Computation........................................................................6
1.5 Computational Intelligence.......................................................................8
1.6 Hybrid Computational Technology..........................................................9
1.7 Application Areas...................................................................................10
1.8 Applications in Industry.........................................................................11
References..............................................................................................12
2 Traditional Problem Definition.....................................................................17
2.1 Introduction to Time Series Analysis.....................................................17
2.2 Traditional Problem Definition...............................................................18
2.2.1 Characteristic Features..............................................................18
2.2.1.1 Stationarity ..................................................................18
2.2.1.2 Linearity ......................................................................20
2.2.1.3 Trend............................................................................20
2.2.1.4 Seasonality...................................................................21
2.2.1.5 Estimation and Elimination of Trend and Seasonality...................................................................21
2.3 Classification of Time Series..................................................................22
2.3.1 Linear Time Series....................................................................23
2.3.2 Nonlinear Time Series...............................................................23
2.3.3 Univariate Time Series..............................................................23
2.3.4 Multivariate Time Series...........................................................24
2.3.5 Chaotic Time Series..................................................................24
2.4 Time Series Analysis..............................................................................25
2.4.1 Objectives of Analysis..............................................................25
2.4.2 Time Series Modelling..............................................................26
2.4.3 Time Series Models...................................................................26
2.5 Regressive Models..................................................................................27
2.5.1 Auto regression Model ..............................................................27
2.5.2 Moving-average Model ............................................................28
2.5.3 ARMA Model...........................................................................28
2.5.4 ARIMA Model..........................................................................29
2.5.5 CARMAX Model......................................................................32
2.5.6 Multivariate Time Series Model................................................33
2.5.7 Linear Time Series Models.......................................................35
2.5.8 Nonlinear Time Series Models..................................................35
2.5.9 Chaotic Time Series Models.....................................................36
2.6 Time-domain Models..............................................................................37
2.6.1 Transfer-function Models..........................................................37
2.6.2 State-space Models....................................................................38
2.7 Frequency-domain Models.....................................................................39
2.8 Model Building.......................................................................................42
2.8.1 Model Identification..................................................................43
2.8.2 Model Estimation......................................................................45
2.8.3 Model Validation and Diagnostic Check..................................48
2.9 Forecasting Methods...............................................................................49
2.9.1 Some Forecasting Issues...........................................................50
2.9.2 Forecasting Using Trend Analysis............................................51
2.9.3 Forecasting Using Regression Approaches...............................51
2.9.4 Forecasting Using the Box-Jenkins Method..............................53
2.9.4.1 Forecasting Using an Autoregressive Model AR(p)....53
2.9.4.2 Forecasting Using a Moving-average Model MA(q)...54
2.9.4.3 Forecasting Using an ARMA Model...........................54
2.9.4.4 Forecasting Using an ARIMA Model..........................56
2.9.4.5 Forecasting Using an CARIMAX Model....................57
2.9.5 Forecasting Using Smoothing...................................................57
2.9.5.1 Forecasting Using a Simple Moving Average.............57
2.9.5.2 Forecasting Using Exponential Smoothing .................58
2.9.5.3 Forecasting Using Adaptive Smoothing......................62
2.9.5.4 Combined Forecast......................................................64
2.10 Application Examples.............................................................................66
2.10.1 Forecasting Nonstationary Processes........................................66
2.10.2 Quality Prediction of Crude Oil................................................67
2.10.3 Production Monitoring and Failure Diagnosis..........................68
2.10.4 Tool Wear Monitoring..............................................................68
2.10.5 Minimum Variance Control......................................................69
2.10.6 General Predictive Control........................................................71
References..............................................................................................74
Selected Reading....................................................................................74
Part II Basic Intelligent Computational Technologies
3 Neural Networks Approach...........................................................................79
3.1 Introduction............................................................................................79
3.2 Basic Network Architecture....................................................................80
3.3 Networks Used for Forecasting..............................................................84
3.3.1 Multilayer Perceptron Networks...............................................84
3.3.2 Radial Basis Function Networks...............................................85
3.3.3 Recurrent Networks ..................................................................87
3.3.4 Counter Propagation Networks.................................................92
3.3.5 Probabilistic Neural Networks..................................................94
3.4 Network Training Methods.....................................................................95
3.4.1 Accelerated Backpropagation Algorithm..................................99
3.5 Forecasting Methodology.....................................................................103
3.5.1 Data Preparation for Forecasting.............................................104
3.5.2 Determination of Network Architecture..................................106
3.5.3 Network Training Strategy......................................................112
3.5.4 Training, Stopping and Evaluation..........................................116
3.6 Forecasting Using Neural Networks.....................................................129
3.6.1 Neural Networks versus Traditional Forecasting....................129
3.6.2 Combining Neural Networks and Traditional Approaches.....131
3.6.3 Nonlinear Combination of Forecasts Using Neural Networks 132
3.6.4 Forecasting of Multivariate Time Series.................................136 References............................................................................................137
Selected Reading..................................................................................142
4 Fuzzy Logic Approach .................................................................................143
4.1 Introduction..........................................................................................143
4.2 Fuzzy Sets and Membership Functions................................................144
4.3 Fuzzy Logic Systems ...........................................................................146
4.3.1 Mamdani Type of Fuzzy Logic Systems.................................148
4.3.2 Takagi-Sugeno Type of Fuzzy Logic Systems........................148
4.3.3 Relational Fuzzy Logic System of Pedrycz.............................149
4.4 Inferencing the Fuzzy Logic System....................................................150
4.4.1 Inferencing a Mamdani-type Fuzzy Model.............................150
4.4.2 Inferencing a Takagi-Sugeno-type Fuzzy Model....................153
4.4.3 Inferencing a (Pedrycz) Relational Fuzzy Model....................154
4.5 Automated Generation of Fuzzy Rule Base..........................................157
4.5.1 The Rules Generation Algorithm............................................157
4.5.2 Modifications Proposed for Automated Rules Generation......162
4.5.3 Estimation of Takagi-Sugeno Rules’ Consequent Parameters...............................................................................166
4.6 Forecasting Time Series Using the Fuzzy Logic Approach..................169
4.6.1 Forecasting Chaotic Time Series: An Example.......................169
4.7 Rules Generation by Clustering............................................................173
4.7.1 Fuzzy Clustering Algorithms for Rule Generation..................173
4.7.1.1 Elements of Clustering Theory .................................174
4.7.1.2 Hard Partition............................................................175
4.7.1.3 Fuzzy Partition...........................................................177
4.7.2 Fuzzy c-means Clustering.......................................................178
4.7.2.1 Fuzzy c-means Algorithm..........................................179
4.7.2.1.1 Parameters of Fuzzy c-means Algorithm....180
4.7.3 Gustafson-Kessel Algorithm...................................................183
4.7.3.1 Gustafson-Kessel Clustering Algorithm....................184
4.7.3.1.1 Parameters of Gustafson-Kessel Algorithm....................................................185
4.7.3.1.2 Interpretation of Cluster Covariance Matrix.........................................................185
4.7.4 Identification of Antecedent Parameters by Fuzzy Clustering................................................................................185
4.7.5 Modelling of a Nonlinear Plant...............................................187
4.8 Fuzzy Model as Nonlinear Forecasts Combiner...................................190
4.9 Concluding Remarks............................................................................193
References............................................................................................193
5 Evolutionary Computation..........................................................................195
5.1 Introduction..........................................................................................195
5.1.1 The Mechanisms of Evolution................................................196
5.1.2 Evolutionary Algorithms.........................................................196
5.2 Genetic Algorithms...............................................................................197
5.2.1 Genetic Operators....................................................................198
5.2.1.1 Selection....................................................................199
5.2.1.2 Reproduction.............................................................199
5.2.1.3 Mutation ....................................................................199
5.2.1.4 Crossover...................................................................201
5.2.2 Auxiliary Genetic Operators...................................................201
5.2.2.1 Fitness Windowing or Scaling...................................201
5.2.3 Real-coded Genetic Algorithms..............................................203
5.2.3.1 Real Genetic Operators..............................................204
5.2.3.1.1 Selection Function......................................204
5.2.3.1.2 Crossover Operators for Real-coded Genetic Algorithms.....................................205
5.2.3.1.3 Mutation Operators.....................................205
5.2.4 Forecasting Examples.............................................................206
5.3 Genetic Programming...........................................................................209
5.3.1 Initialization............................................................................210
5.3.2 Execution of Algorithm...........................................................211
5.3.3 Fitness Measure.......................................................................211
5.3.4 Improved Genetic Versions.....................................................211
5.3.5 Applications............................................................................212
5.4 Evolutionary Strategies.........................................................................212
5.4.1 Applications to Real-world Problems ....................................213
5.5 Evolutionary Programming ..................................................................214
5.5.1 Evolutionary Programming Mechanism ................................215
5.6 Differential Evolution ..........................................................................215
5.6.1 First Variant of Differential Evolution (DE1).........................216
5.6.2 Second Variant of Differential Evolution (DE2).....................218 References............................................................................................218
Part III Hybrid Computational Technologies
6 Neuro-fuzzy Approach.................................................................................223
6.1 Motivation for Technology Merging....................................................223
6.2 Neuro-fuzzy Modelling ........................................................................224
6.2.1 Fuzzy Neurons........................................................................227
6.2.1.1 AND Fuzzy Neuron...................................................228
6.2.1.2 OR Fuzzy Neuron......................................................229
6.3 Neuro-fuzzy System Selection for Forecasting....................................230
6.4 Takagi-Sugeno-type Neuro-fuzzy Network..........................................232
6.4.1 Neural Network Representation of Fuzzy Logic Systems.......233
6.4.2 Training Algorithm for Neuro-fuzzy Network........................234
6.4.2.1 Backpropagation Training of Takagi-Sugeno-type Neuro-fuzzy Network................................................234
6.4.2.2 Improved Backpropagation Training Algorithm.......238
6.4.2.3 Levenberg-Marquardt Training Algorithm................239
6.4.2.3.1 Computation of Jacobian Matrix ...............241
6.4.2.4 Adaptive Learning Rate and Oscillation Control ......246
6.5 Comparison of Radial Basis Function Network and Neuro-fuzzy Network ..........................................................................247
6.6 Comparison of Neural Network and Neuro-fuzzy Network Training..248
6.7 Modelling and Identification of Nonlinear Dynamics .........................249
6.7.1 Short-term Forecasting of Electrical load ...............................249
6.7.2 Prediction of Chaotic Time Series...........................................253
6.7.3 Modelling and Prediction of Wang Data.................................258
6.8 Other Engineering Application Examples............................................264
6.8.1 Application of Neuro-fuzzy Modelling to Materials Property Prediction .................................................265
6.8.1.1 Property Prediction for C-Mn Steels ..........................266
6.8.1.2 Property Prediction for C-Mn-Nb Steels ....................266
6.8.2 Correction of Pyrometer Reading ...........................................266
6.8.3 Application for Tool Wear Monitoring ..................................268
6.9 Concluding Remarks............................................................................270 References............................................................................................271
7 Transparent Fuzzy/Neuro-fuzzy Modelling ..............................................275
7.1 Introduction .........................................................................................275
7.2 Model Transparency and Compactness................................................276
7.3 Fuzzy Modelling with Enhanced Transparency....................................277
7.3.1 Redundancy in Numerical Data-driven Modelling .................277
7.3.2 Compact and Transparent Modelling Scheme ........................279
7.4 Similarity Between Fuzzy Sets.............................................................281
7.4.1 Similarity Measure..................................................................282
7.4.2 Similarity-based Rule Base Simplification .............................282
7.5 Simplification of Rule Base..................................................................285
7.5.1 Merging Similar Fuzzy Sets....................................................287
7.5.2 Removing Irrelevant Fuzzy Sets.............................................289
7.5.3 Removing Redundant Inputs...................................................290
7.5.4 Merging Rules ........................................................................290
7.6 Rule Base Simplification Algorithms ..................................................291
7.6.1 Iterative Merging.....................................................................292
7.6.2 Similarity Relations.................................................................294
7.7 Model Competitive Issues: Accuracy versus Complexity....................296
7.8 Application Examples...........................................................................299
7.9 Concluding Remarks............................................................................302 References............................................................................................302
8 Evolving Neural and Fuzzy Systems...........................................................305
8.1 Introduction..........................................................................................305
8.1.1 Evolving Neural Networks......................................................305
8.1.1.1 Evolving Connection Weights...................................306
8.1.1.2 Evolving the Network Architecture...........................309
8.1.1.3 Evolving the Pure Network Architecture...................310
8.1.1.4 Evolving Complete Network.....................................311
8.1.1.5 Evolving the Activation Function..............................312
8.1.1.6 Application Examples................................................313
8.1.2 Evolving Fuzzy Logic Systems...............................................313 References............................................................................................317
9 Adaptive Genetic Algorithms.......................................................................321
9.1 Introduction..........................................................................................321
9.2 Genetic Algorithm Parameters to Be Adapted......................................322
9.3 Probabilistic Control of Genetic Algorithm Parameters.......................323
9.4 Adaptation of Population Size..............................................................327
9.5 Fuzzy-logic-controlled Genetic Algorithms.........................................329
9.6 Concluding Remarks............................................................................330 References............................................................................................330
Part IV Recent Developments
10 State of the Art and Development Trends..................................................335
10.1 Introduction..........................................................................................335
10.2 Support Vector Machines.....................................................................337
10.2.1 Data-dependent Representation...............................................342
10.2.2 Machine Implementation.........................................................343
10.2.3 Applications............................................................................344
10.3 Wavelet Networks................................................................................345 10.3.1 Wavelet Theory.......................................................................345
10.3.2 Wavelet Neural Networks.......................................................346
10.3.3 Applications............................................................................349
10.4 Fractally Configured Neural Networks.................................................350
10.5 Fuzzy Clustering...................................................................................352 10.5.1 Fuzzy Clustering Using Kohonen Networks...........................353
10.5.2 Entropy-based Fuzzy Clustering.............................................355
10.5.2.1 Entropy Measure for Cluster Estimation...................356
10.5.2.1 The Entropy Measure ..................................356
10.5.2.2 Fuzzy Clustering Based on Entropy Measure............358
10.5.2.3 Fuzzy Model Identification Using Entropy-based Fuzzy Clustering................................359 References............................................................................................360
Index....................................................................................................................363
· · · · · · (收起)
1 Computational Intelligence: An Introduction................................................3
1.1 Introduction..............................................................................................3
1.2 Soft Computing.........................................................................................3
1.3 Probabilistic Reasoning............................................................................4
1.4 Evolutionary Computation........................................................................6
1.5 Computational Intelligence.......................................................................8
1.6 Hybrid Computational Technology..........................................................9
1.7 Application Areas...................................................................................10
1.8 Applications in Industry.........................................................................11
References..............................................................................................12
2 Traditional Problem Definition.....................................................................17
2.1 Introduction to Time Series Analysis.....................................................17
2.2 Traditional Problem Definition...............................................................18
2.2.1 Characteristic Features..............................................................18
2.2.1.1 Stationarity ..................................................................18
2.2.1.2 Linearity ......................................................................20
2.2.1.3 Trend............................................................................20
2.2.1.4 Seasonality...................................................................21
2.2.1.5 Estimation and Elimination of Trend and Seasonality...................................................................21
2.3 Classification of Time Series..................................................................22
2.3.1 Linear Time Series....................................................................23
2.3.2 Nonlinear Time Series...............................................................23
2.3.3 Univariate Time Series..............................................................23
2.3.4 Multivariate Time Series...........................................................24
2.3.5 Chaotic Time Series..................................................................24
2.4 Time Series Analysis..............................................................................25
2.4.1 Objectives of Analysis..............................................................25
2.4.2 Time Series Modelling..............................................................26
2.4.3 Time Series Models...................................................................26
2.5 Regressive Models..................................................................................27
2.5.1 Auto regression Model ..............................................................27
2.5.2 Moving-average Model ............................................................28
2.5.3 ARMA Model...........................................................................28
2.5.4 ARIMA Model..........................................................................29
2.5.5 CARMAX Model......................................................................32
2.5.6 Multivariate Time Series Model................................................33
2.5.7 Linear Time Series Models.......................................................35
2.5.8 Nonlinear Time Series Models..................................................35
2.5.9 Chaotic Time Series Models.....................................................36
2.6 Time-domain Models..............................................................................37
2.6.1 Transfer-function Models..........................................................37
2.6.2 State-space Models....................................................................38
2.7 Frequency-domain Models.....................................................................39
2.8 Model Building.......................................................................................42
2.8.1 Model Identification..................................................................43
2.8.2 Model Estimation......................................................................45
2.8.3 Model Validation and Diagnostic Check..................................48
2.9 Forecasting Methods...............................................................................49
2.9.1 Some Forecasting Issues...........................................................50
2.9.2 Forecasting Using Trend Analysis............................................51
2.9.3 Forecasting Using Regression Approaches...............................51
2.9.4 Forecasting Using the Box-Jenkins Method..............................53
2.9.4.1 Forecasting Using an Autoregressive Model AR(p)....53
2.9.4.2 Forecasting Using a Moving-average Model MA(q)...54
2.9.4.3 Forecasting Using an ARMA Model...........................54
2.9.4.4 Forecasting Using an ARIMA Model..........................56
2.9.4.5 Forecasting Using an CARIMAX Model....................57
2.9.5 Forecasting Using Smoothing...................................................57
2.9.5.1 Forecasting Using a Simple Moving Average.............57
2.9.5.2 Forecasting Using Exponential Smoothing .................58
2.9.5.3 Forecasting Using Adaptive Smoothing......................62
2.9.5.4 Combined Forecast......................................................64
2.10 Application Examples.............................................................................66
2.10.1 Forecasting Nonstationary Processes........................................66
2.10.2 Quality Prediction of Crude Oil................................................67
2.10.3 Production Monitoring and Failure Diagnosis..........................68
2.10.4 Tool Wear Monitoring..............................................................68
2.10.5 Minimum Variance Control......................................................69
2.10.6 General Predictive Control........................................................71
References..............................................................................................74
Selected Reading....................................................................................74
Part II Basic Intelligent Computational Technologies
3 Neural Networks Approach...........................................................................79
3.1 Introduction............................................................................................79
3.2 Basic Network Architecture....................................................................80
3.3 Networks Used for Forecasting..............................................................84
3.3.1 Multilayer Perceptron Networks...............................................84
3.3.2 Radial Basis Function Networks...............................................85
3.3.3 Recurrent Networks ..................................................................87
3.3.4 Counter Propagation Networks.................................................92
3.3.5 Probabilistic Neural Networks..................................................94
3.4 Network Training Methods.....................................................................95
3.4.1 Accelerated Backpropagation Algorithm..................................99
3.5 Forecasting Methodology.....................................................................103
3.5.1 Data Preparation for Forecasting.............................................104
3.5.2 Determination of Network Architecture..................................106
3.5.3 Network Training Strategy......................................................112
3.5.4 Training, Stopping and Evaluation..........................................116
3.6 Forecasting Using Neural Networks.....................................................129
3.6.1 Neural Networks versus Traditional Forecasting....................129
3.6.2 Combining Neural Networks and Traditional Approaches.....131
3.6.3 Nonlinear Combination of Forecasts Using Neural Networks 132
3.6.4 Forecasting of Multivariate Time Series.................................136 References............................................................................................137
Selected Reading..................................................................................142
4 Fuzzy Logic Approach .................................................................................143
4.1 Introduction..........................................................................................143
4.2 Fuzzy Sets and Membership Functions................................................144
4.3 Fuzzy Logic Systems ...........................................................................146
4.3.1 Mamdani Type of Fuzzy Logic Systems.................................148
4.3.2 Takagi-Sugeno Type of Fuzzy Logic Systems........................148
4.3.3 Relational Fuzzy Logic System of Pedrycz.............................149
4.4 Inferencing the Fuzzy Logic System....................................................150
4.4.1 Inferencing a Mamdani-type Fuzzy Model.............................150
4.4.2 Inferencing a Takagi-Sugeno-type Fuzzy Model....................153
4.4.3 Inferencing a (Pedrycz) Relational Fuzzy Model....................154
4.5 Automated Generation of Fuzzy Rule Base..........................................157
4.5.1 The Rules Generation Algorithm............................................157
4.5.2 Modifications Proposed for Automated Rules Generation......162
4.5.3 Estimation of Takagi-Sugeno Rules’ Consequent Parameters...............................................................................166
4.6 Forecasting Time Series Using the Fuzzy Logic Approach..................169
4.6.1 Forecasting Chaotic Time Series: An Example.......................169
4.7 Rules Generation by Clustering............................................................173
4.7.1 Fuzzy Clustering Algorithms for Rule Generation..................173
4.7.1.1 Elements of Clustering Theory .................................174
4.7.1.2 Hard Partition............................................................175
4.7.1.3 Fuzzy Partition...........................................................177
4.7.2 Fuzzy c-means Clustering.......................................................178
4.7.2.1 Fuzzy c-means Algorithm..........................................179
4.7.2.1.1 Parameters of Fuzzy c-means Algorithm....180
4.7.3 Gustafson-Kessel Algorithm...................................................183
4.7.3.1 Gustafson-Kessel Clustering Algorithm....................184
4.7.3.1.1 Parameters of Gustafson-Kessel Algorithm....................................................185
4.7.3.1.2 Interpretation of Cluster Covariance Matrix.........................................................185
4.7.4 Identification of Antecedent Parameters by Fuzzy Clustering................................................................................185
4.7.5 Modelling of a Nonlinear Plant...............................................187
4.8 Fuzzy Model as Nonlinear Forecasts Combiner...................................190
4.9 Concluding Remarks............................................................................193
References............................................................................................193
5 Evolutionary Computation..........................................................................195
5.1 Introduction..........................................................................................195
5.1.1 The Mechanisms of Evolution................................................196
5.1.2 Evolutionary Algorithms.........................................................196
5.2 Genetic Algorithms...............................................................................197
5.2.1 Genetic Operators....................................................................198
5.2.1.1 Selection....................................................................199
5.2.1.2 Reproduction.............................................................199
5.2.1.3 Mutation ....................................................................199
5.2.1.4 Crossover...................................................................201
5.2.2 Auxiliary Genetic Operators...................................................201
5.2.2.1 Fitness Windowing or Scaling...................................201
5.2.3 Real-coded Genetic Algorithms..............................................203
5.2.3.1 Real Genetic Operators..............................................204
5.2.3.1.1 Selection Function......................................204
5.2.3.1.2 Crossover Operators for Real-coded Genetic Algorithms.....................................205
5.2.3.1.3 Mutation Operators.....................................205
5.2.4 Forecasting Examples.............................................................206
5.3 Genetic Programming...........................................................................209
5.3.1 Initialization............................................................................210
5.3.2 Execution of Algorithm...........................................................211
5.3.3 Fitness Measure.......................................................................211
5.3.4 Improved Genetic Versions.....................................................211
5.3.5 Applications............................................................................212
5.4 Evolutionary Strategies.........................................................................212
5.4.1 Applications to Real-world Problems ....................................213
5.5 Evolutionary Programming ..................................................................214
5.5.1 Evolutionary Programming Mechanism ................................215
5.6 Differential Evolution ..........................................................................215
5.6.1 First Variant of Differential Evolution (DE1).........................216
5.6.2 Second Variant of Differential Evolution (DE2).....................218 References............................................................................................218
Part III Hybrid Computational Technologies
6 Neuro-fuzzy Approach.................................................................................223
6.1 Motivation for Technology Merging....................................................223
6.2 Neuro-fuzzy Modelling ........................................................................224
6.2.1 Fuzzy Neurons........................................................................227
6.2.1.1 AND Fuzzy Neuron...................................................228
6.2.1.2 OR Fuzzy Neuron......................................................229
6.3 Neuro-fuzzy System Selection for Forecasting....................................230
6.4 Takagi-Sugeno-type Neuro-fuzzy Network..........................................232
6.4.1 Neural Network Representation of Fuzzy Logic Systems.......233
6.4.2 Training Algorithm for Neuro-fuzzy Network........................234
6.4.2.1 Backpropagation Training of Takagi-Sugeno-type Neuro-fuzzy Network................................................234
6.4.2.2 Improved Backpropagation Training Algorithm.......238
6.4.2.3 Levenberg-Marquardt Training Algorithm................239
6.4.2.3.1 Computation of Jacobian Matrix ...............241
6.4.2.4 Adaptive Learning Rate and Oscillation Control ......246
6.5 Comparison of Radial Basis Function Network and Neuro-fuzzy Network ..........................................................................247
6.6 Comparison of Neural Network and Neuro-fuzzy Network Training..248
6.7 Modelling and Identification of Nonlinear Dynamics .........................249
6.7.1 Short-term Forecasting of Electrical load ...............................249
6.7.2 Prediction of Chaotic Time Series...........................................253
6.7.3 Modelling and Prediction of Wang Data.................................258
6.8 Other Engineering Application Examples............................................264
6.8.1 Application of Neuro-fuzzy Modelling to Materials Property Prediction .................................................265
6.8.1.1 Property Prediction for C-Mn Steels ..........................266
6.8.1.2 Property Prediction for C-Mn-Nb Steels ....................266
6.8.2 Correction of Pyrometer Reading ...........................................266
6.8.3 Application for Tool Wear Monitoring ..................................268
6.9 Concluding Remarks............................................................................270 References............................................................................................271
7 Transparent Fuzzy/Neuro-fuzzy Modelling ..............................................275
7.1 Introduction .........................................................................................275
7.2 Model Transparency and Compactness................................................276
7.3 Fuzzy Modelling with Enhanced Transparency....................................277
7.3.1 Redundancy in Numerical Data-driven Modelling .................277
7.3.2 Compact and Transparent Modelling Scheme ........................279
7.4 Similarity Between Fuzzy Sets.............................................................281
7.4.1 Similarity Measure..................................................................282
7.4.2 Similarity-based Rule Base Simplification .............................282
7.5 Simplification of Rule Base..................................................................285
7.5.1 Merging Similar Fuzzy Sets....................................................287
7.5.2 Removing Irrelevant Fuzzy Sets.............................................289
7.5.3 Removing Redundant Inputs...................................................290
7.5.4 Merging Rules ........................................................................290
7.6 Rule Base Simplification Algorithms ..................................................291
7.6.1 Iterative Merging.....................................................................292
7.6.2 Similarity Relations.................................................................294
7.7 Model Competitive Issues: Accuracy versus Complexity....................296
7.8 Application Examples...........................................................................299
7.9 Concluding Remarks............................................................................302 References............................................................................................302
8 Evolving Neural and Fuzzy Systems...........................................................305
8.1 Introduction..........................................................................................305
8.1.1 Evolving Neural Networks......................................................305
8.1.1.1 Evolving Connection Weights...................................306
8.1.1.2 Evolving the Network Architecture...........................309
8.1.1.3 Evolving the Pure Network Architecture...................310
8.1.1.4 Evolving Complete Network.....................................311
8.1.1.5 Evolving the Activation Function..............................312
8.1.1.6 Application Examples................................................313
8.1.2 Evolving Fuzzy Logic Systems...............................................313 References............................................................................................317
9 Adaptive Genetic Algorithms.......................................................................321
9.1 Introduction..........................................................................................321
9.2 Genetic Algorithm Parameters to Be Adapted......................................322
9.3 Probabilistic Control of Genetic Algorithm Parameters.......................323
9.4 Adaptation of Population Size..............................................................327
9.5 Fuzzy-logic-controlled Genetic Algorithms.........................................329
9.6 Concluding Remarks............................................................................330 References............................................................................................330
Part IV Recent Developments
10 State of the Art and Development Trends..................................................335
10.1 Introduction..........................................................................................335
10.2 Support Vector Machines.....................................................................337
10.2.1 Data-dependent Representation...............................................342
10.2.2 Machine Implementation.........................................................343
10.2.3 Applications............................................................................344
10.3 Wavelet Networks................................................................................345 10.3.1 Wavelet Theory.......................................................................345
10.3.2 Wavelet Neural Networks.......................................................346
10.3.3 Applications............................................................................349
10.4 Fractally Configured Neural Networks.................................................350
10.5 Fuzzy Clustering...................................................................................352 10.5.1 Fuzzy Clustering Using Kohonen Networks...........................353
10.5.2 Entropy-based Fuzzy Clustering.............................................355
10.5.2.1 Entropy Measure for Cluster Estimation...................356
10.5.2.1 The Entropy Measure ..................................356
10.5.2.2 Fuzzy Clustering Based on Entropy Measure............358
10.5.2.3 Fuzzy Model Identification Using Entropy-based Fuzzy Clustering................................359 References............................................................................................360
Index....................................................................................................................363
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书名《计算智能在时间序列预测中的应用》立刻激发了我对数据背后隐藏的智能力量的探索欲。我设想，这本书不仅仅是在介绍技术，更是在揭示一种预测的哲学——如何让机器像人类一样“思考”和“学习”，从而理解和预测那些充满不确定性的时间序列。我非常好奇，书中会如何阐述“模糊逻辑”在处理时间序列中的不确定性和主观性方面所扮演的角色？例如，在描述天气变化时，我们常常会用到“温暖”、“微风”等模糊词汇，模糊逻辑是否能够将这些概念量化，并融入预测模型中，从而提升预测的直观性和实用性？此外，我还在期待书中能深入探讨“遗传算法”或“粒子群优化”等进化计算方法，如何在海量的时间序列数据中，通过迭代和搜索，找到最优的预测模型参数，就像生物在自然选择中不断进化一样。这种通过“试错”和“优化”来实现智能预测的过程，本身就充满了引人入胜的魅力，我希望这本书能为我展现这一过程的细节与奥秘。
评分当我看到《计算智能在时间序列预测中的应用》这个书名时，一股强烈的求知欲便油然而生。我设想，这本书将是一次关于数据智能的深度探险，探索如何利用计算智能技术，揭示时间序列数据中隐藏的奥秘，并预测未来的走向。我尤其期待书中对“粒子群优化”或“蚁群优化”等群体智能算法在时间序列预测中的应用进行详细阐述。这些算法是如何模拟自然界生物的行为，并在复杂的数据空间中进行搜索，找到最优的预测模型参数的？同时，我也对“核方法”在时间序列预测中的潜力抱有浓厚的兴趣，例如“支持向量回归（SVR）”，它如何通过核技巧将数据映射到高维空间，从而解决非线性预测问题？我希望这本书能够提供清晰的理论框架和丰富的实践案例，让我能够理解如何将这些计算智能的精髓，转化为解决实际问题的强大武器。
评分“计算智能”——这个词汇本身就带着一种神秘而强大的力量，尤其当它被冠以“时间序列预测”之名时，其吸引力更是指数级增长。我脑海中勾勒出的画面是，本书不仅仅是理论的堆砌，而是将那些抽象的计算智能原理，通过生动的案例和严谨的数学推导，转化为可操作的预测工具。我迫不及待地想知道，作者是如何将模糊系统应用于处理那些带有不确定性、难以量化的时间序列数据的？又或者，他们是如何利用遗传算法的进化思想，来优化预测模型的参数，使其能够不断地“学习”和“进化”，以适应不断变化的数据环境？这种“智能”的引入，无疑将预测的精度和鲁棒性提升到了一个新的高度。我设想，书中或许会涉及对不同计算智能方法的性能进行对比分析，帮助读者理解它们的优劣势，以及在何种场景下选择何种方法更为合适。这种实践性的指导，对于希望将理论知识转化为实际应用的研究者和工程师来说，无疑是弥足珍贵的。它不仅仅是关于“如何预测”，更是关于“如何用智能的方式预测”。
评分一本名为《计算智能在时间序列预测中的应用》的书籍，即使我还没翻开它，仅仅从书名就能够感受到它所承载的厚重与前沿。想象一下，当复杂的、变幻莫测的时间序列数据，遇上那些能够模拟生物智能、学习和适应的计算智能技术，会激荡出怎样的火花？这本书似乎提供了一个窥探这种“智能”碰撞的窗口。我脑海中浮现出的是，数据科学家们如何在纷繁的数据洪流中，利用这些先进的算法，识别隐藏的模式、预测未来的趋势。这不仅仅是简单的数值推演，更是一种对事物发展规律的深度洞察和智能模拟。我尤其好奇的是，书中会对哪些具体的计算智能方法进行深入剖析？是经典的神经网络，还是更具启发性的模糊逻辑、遗传算法，亦或是近几年大放异彩的深度学习模型？我期待能看到它们如何被巧妙地运用到股票市场的波动预测、天气变化的趋势分析、经济指标的短期展望，甚至是疾病传播的动态模拟等各种实际场景中。每一项应用都蕴含着巨大的价值和挑战，而这本书，我预感，就是那把解锁这些挑战的钥匙，它将引导我们穿越数据迷雾，抵达智能预测的彼岸。
评分《计算智能在时间序列预测中的应用》——这个书名仿佛一个指向未来的罗盘，指引我进入一个充满无限可能的数据世界。我脑海中构筑的画面是，通过这本书，我将学会如何“激活”数据的智能，让它们不再是沉寂的数字，而是能够“讲述”未来故事的精灵。我非常好奇，书中是否会详细讲解如何利用“深度信念网络（DBN）”或“卷积神经网络（CNN）”等深度学习模型，来处理具有复杂时空依赖性的时间序列数据？例如，在处理视频流或传感器网络数据时，这些模型将如何发挥其强大的特征提取能力？同时，我也对“贝叶斯网络”在时间序列预测中的应用充满期待，它如何能够有效地处理不确定性，并进行因果推理，从而做出更具解释性的预测？我希望这本书能成为我进入计算智能预测领域的敲门砖，让我能够掌握那些能够洞察先机、预见未来的智能工具。
评分当我看到《计算智能在时间序列预测中的应用》这个书名时，脑海中立即浮现出一幅画面：数据科学家们如同炼金术士，将原始的时间序列数据投入到计算智能的熔炉中，经过巧妙的设计和精密的调控，最终炼制出预测未来的“黄金”。这本书，我预感，就是他们手中的那本秘籍。我尤其好奇的是，书中会如何深入浅出地解释那些复杂的计算智能算法，比如“神经网络”是如何通过层层传递和激活，来学习时间序列中的非线性模式的？又或者，在面对具有周期性或季节性特征的时间序列时，是否会介绍一些专门为此设计的计算智能方法，例如利用傅里叶变换或小波变换与神经网络相结合的预测模型？我期待这本书能提供清晰的理论框架，并辅以详实的案例分析，让我能够理解这些智能方法是如何一步步构建起来，并最终用于解决诸如能源需求预测、交通流量预测等实际问题。这种从理论到实践的无缝对接，正是吸引我的关键所在。
评分《计算智能在时间序列预测中的应用》——这个书名如同一个邀请，邀请我去探索数据背后潜藏的“智慧”。我脑海中浮现的，不仅仅是冰冷的算法，而是那些能够模拟人类学习过程、不断优化自身预测能力的智能系统。我尤其好奇，书中会如何解析“人工神经网络”在处理非线性时间序列方面的独特优势？例如，如何通过调整网络的结构、激活函数以及训练策略，使其能够捕捉到数据中那些微妙的、不易察觉的模式？此外，我也对“模糊逻辑”在时间序列预测中的应用非常感兴趣，它如何能够处理那些不精确、不确定性强的数据，并将其转化为可用于预测的语言？这本书，我期待它能为我揭示如何将这些“智能”的特性，巧妙地融入到时间序列的预测模型中，从而提高预测的精度和鲁棒性，帮助我们在瞬息万变的世界中，做出更明智的决策。
评分当我看到《计算智能在时间序列预测中的应用》这个书名时，脑海中立刻联想到的是那些看似杂乱无章，实则蕴含着规律的时间序列数据。无论是股票市场的日K线图，还是气象站的每小时温度记录，亦或是传感器每秒的读数，它们都如同生命体的脉搏，跳动着信息的节律。而“计算智能”则如同拥有了洞悉这脉搏的能力，它能够学习、记忆、推理，甚至创造。我好奇的是，书中是如何将这些“智能”的特性，赋予到预测模型中的？是否会介绍一些能够从历史数据中自动提取复杂非线性模式的算法？例如，如何利用人工神经网络，特别是深度学习中的长短期记忆网络（LSTM）或门控循环单元（GRU），来捕捉时间序列中的长期依赖关系？又或者，本书是否会探讨如何结合多种计算智能技术，形成一个更强大、更具鲁棒性的混合预测系统？我期待看到的是，那些晦涩难懂的算法，在书中化身为解决实际问题的强大工具，帮助我们更准确地预测未来，从而做出更明智的决策。
评分一本名为《计算智能在时间序列预测中的应用》的书籍，单单是这个书名就已经在我脑海中勾勒出了一幅宏伟的蓝图。我设想，这本书将不仅仅是算法的堆砌，而是关于如何赋予机器“洞察”时间序列数据背后规律的能力。我非常好奇，书中会如何阐述“深度学习”在时间序列预测中的突破性进展？例如，对于那些包含复杂长距离依赖关系的序列，如经济周期或气候变化，递归神经网络（RNN）及其变种（如LSTM、GRU）是如何捕捉这些信息的？同时，我也对“集成学习”在时间序列预测中的应用充满期待，如何将多个计算智能模型的结果进行融合，以达到更稳定、更准确的预测效果？这本书，我期待它能成为一座桥梁，连接理论的严谨与实践的灵活，让我能够理解如何将这些先进的计算智能技术，有效地应用于解决诸如电力负荷预测、交通流量预测等现实世界中的复杂问题。
评分“计算智能”与“时间序列预测”的结合，在我看来，是一种对未来趋势的深度探索，而《计算智能在时间序列预测中的应用》这本书，无疑是这场探索之旅的向导。我设想，本书会带领我穿越繁复的数据噪音，去发现隐藏在时间序列背后那股看不见却又切实存在的“智能”驱动力。我特别渴望了解，书中是否会详细介绍如何利用“支持向量机（SVM）”来处理那些具有复杂边界和高维度的时间序列数据？例如，在金融市场预测中，SVM是否能够有效地识别出市场趋势的转折点？同时，我也对“专家系统”或“模糊推理系统”在时间序列预测中的应用抱有浓厚的兴趣，它们如何将领域专家的知识和经验融入预测模型，形成一种“智能”的决策机制？我期待这本书能够提供一种全新的视角，让我理解如何将这些具有学习和推理能力的计算智能技术，转化为能够预测未来走向的强大工具，从而在充满不确定性的世界中，找到一丝可预测的规律。
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