Neural Networks and Learning Machines, 3/e (Hardcover)

Fluid and authoritative, this well-organized book represents the first comprehensive treatment of neural networks and learning machines from an engineering perspective, providing extensive, state-of-the-art coverage that will expose readers to the myriad facets of neural networks and help them appreciate the technology's origin, capabilities, and potential applications. Examines all the important aspects of this emerging technology, covering the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementation. Integrates computer experiments throughout to demonstrate how neural networks are designed and perform in practice. Chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary all reinforce concepts throughout. New chapters delve into such areas as support vector machines, and reinforcement learning/neurodynamic programming,  Rosenblatt’s Perceptron, Least-Mean-Square Algorithm, Regularization Theory, Kernel Methods and Radial-Basis function networks (RBF), and Bayseian Filtering for State Estimation of Dynamic Systems. An entire chapter of case studies illustrates the real-life, practical applications of neural networks. A highly detailed bibliography is included for easy reference. For professional engineers and research scientists.

Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/

這本組織良好的書籍具有流暢且權威的風格，從工程角度全面介紹了神經網絡和學習機器，提供了廣泛而最新的內容，讓讀者了解神經網絡的起源、能力和潛在應用。該書涵蓋了這一新興技術的所有重要方面，包括學習過程、反向傳播、徑向基函數、循環網絡、自組織系統、模塊化網絡、時間處理、神經動力學和VLSI實現。書中通過計算機實驗展示了神經網絡的設計和實際應用。章節目標、問題、實例、參考文獻、照片、插圖和詳盡的詞彙表都有助於加深概念的理解。新的章節涵蓋了支持向量機、強化學習/神經動力規劃、羅森布拉特的感知器、最小均方算法、正則化理論、核方法和徑向基函數網絡，以及貝葉斯濾波用於動態系統的狀態估計。一整章的案例研究展示了神經網絡的實際應用。書中還包含了詳細的參考文獻，方便查閱。適用於專業工程師和研究科學家。

本書中的計算機實驗所使用的Matlab代碼可在以下網址下載：http://www.pearsonhighered.com/haykin/