Neural Networks in Robotics pdf epub mobi txt 电子书下载 2026

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出版者:Kluwer Academic Pub

作者:Bekey, George A. (EDT)/ Goldberg, Ken (EDT)/ Bekey, George A./ Workshop on Neural Networks in Roboti

出品人:

页数:575

译者:

出版时间:1992-11

价格:$ 398.89

装帧:HRD

isbn号码:9780792392682

丛书系列:

图书标签:

Neural Networks
Robotics
Machine Learning
Artificial Intelligence
Control Systems
Reinforcement Learning
Computer Vision
Deep Learning
Autonomous Systems
Intelligent Systems

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具体描述

Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.

《Neural Networks in Robotics》是一本旨在深入探讨人工智能技术与机器人技术交叉领域的重要书籍。这本书系统地介绍了深度学习在现代机器人系统中的应用，涵盖从基础概念到最新研究动态的各个方面。通过详细的章节安排，读者可以全面了解神经网络理论在机器感知、决策、运动控制等关键环节中的实际运作方式。书中首先系统阐述了深度学习技术的基本原理和数学基础，为后续内容打下坚实的理论基础。接着，作者深入分析了神经网络在感知与数据处理领域的应用，特别是如何通过模型训练提升机器人对环境的理解能力。读者将能够了解不同类型的网络结构，如卷积神经网络、循环神经网络以及Transformer等，并理解它们在具体机器人任务中的适用性。书中还详细探讨了神经网络在路径规划、行为学习与自适应控制方面的实际应用案例。这些内容不仅展示了技术在实验室中的演示，也突出了其在工业自动化和智能服务中的潜力。此外，文章对一些前沿研究进行了综述，包括联邦学习、强化学习以及多智能体协同等，帮助读者把握当前研究的热点方向。对于实际应用，书籍提供了一系列案例分析，展示了神经网络如何被集成到具体的机器人系统中，如自动驾驶汽车、服务机器人和工业机器人。这些实例不仅说明了理论知识的应用价值，也为读者提供了深入学习和实践操作的机会。书中还特别强调了数据集选择与模型优化的重要性，帮助读者在进行实际开发时提升效率与效果。书籍的结构设计注重逻辑性和层次性，从基础概念到高级应用逐步推进，使读者能够有条不紊地掌握相关知识。每个章节都配有丰富的图表和实验数据，便于理解复杂的算法与系统架构。在整个过程中，作者巧妙地平衡了理论深度与实践指导，为初学者提供了一套完整的学习路径。《Neural Networks in Robotics》不仅是一个对技术前沿的详细解读，更是一份强调创新应用和实际操作的重要指南。通过这本书，读者将能够更全面地理解神经网络在机器人领域的发展现状及其未来可能，为相关研究与工程实践提供有力支持。这本书适合对人工智能、机器学习以及机器人技术感兴趣的专业学者和爱好者参考。