本书是一部优秀的教材，着重讲述人工神经网络基本原理以及如何运用各种神经计算技术来解决科学和工程领域中的现实问题：模式识别、最优化、事件分类、非线性系统的控制和识别以及统计分析等。
　　算法——大多数训练算法都用上下框线框出，便于读者查找 MATLAB函数——一些训练算法有一个附带的MATLAB函数实现(在文中用黑体字显示)。代码部分相对简短，仅用几分钟就可以输入MATLAB MATLAB Toolbox——书中大量使用MATLAB的Neural Network Toolbox来举例说明某些神经计算概念
Web站点——登录本书的Web站点http：／／www．mhhe．com／engcs／electrical／ham可获取最新、最全面的信息示例——在大多数章节中都给出了详尽的示例，阐释重要的神经计算概念
　　习题集——每章最后都给出大量应用神经计算技术的习题。一些习题需要使用MATLAB和MATLAB的Neural Network Toolbox。在某些情况下，还提供了MATLAB函数代码附录——附录A全面介绍了神经计算的数学基础.

About the Authors
Preface
Acknowledgments
List of Important Symbols and Operators
List of Important Abbreviations
PARTI　Fundamental Neurocomputing Concepts and
Selected Neural Network Architectures and
Learning Rules
1　Introduction to Neurocomputing
1.1　What Is Neurocomputing
1.2　Historical Notes
1.3　Neurocomputing and Neuroscience
1.4　Classification of Neural Networks
1.5　Guide to the Book
References
2　Fundamental Neurocomputing Concepts
2.1　Introduction
2.2　Basic Models of Artificial Neurons
2.3　Basic Activation Functions
2.4　Hopfield Model of the Artificial Neuron
2.5　Adaline and Madaline
2.6　Simple Perceptron
2.7　Feedforward Multilayer Perceptron
2.8　Overview of Basic Learning Rules for a Single Neuron
2.9　Data Preprocessing
Problems
References
Mapping Networks
3.1　Introduction
3.2　Associative Memory Networks
3.3　Backpropagation Learning Algorithms
3.4　Accelerated Learning Backpropagation Algorithms
3.5　Counterpropagation
3.6　Radial Basis Function Neural Networks
Problems
References
4　Self-Organizing Networks
4.1　Introduction
4.2　Kohonen Self-Organizing Map
4.3　Learning Vector Quantization
4.4　Adaptive Resonance Theory (ART) Neural Networks
Problems
References
5　Recurrent Networks and Temporal Feedforward
Networks
5.1　Introduction
5.2　Overview of Recurrent Neural Networks
5.3　Hopfield Associative Memory
5.4　Simulated Annealing
5.5　Boltzmann Machine
5.6　Overview of Temporal Feedforward Networks
5.7　Simple Recurrent Network
5.8　Time-Delay Neural Networks
5.9　Distributed Time-Lagged Feedforward Neural
Networks
Problems
References
PART II Applications of Neurocomputing
6　Neural Networks for Optimization Problems
6.1　Introduction
6.2　Neural Networks for Linear Programming Problems
6.3　Neural Networks for Quadratic Programming
Problems
6.4　Neural Networks for Nonlinear Continuous
Constrained Optimization Problems
Problems
References
Solving Matrix Algebra Problems with Neural Networks
7.1　Introduction
7.2　Inverse and Pseudoinverse of a Matrix
7.3　LU Decomposition
7.4　QR Factorization
7.5　Schur Decomposition
7.6　Spectral Factorization - Eigenvalue Decomposition
(EVD) (Symmetric Eigenvalue Problem)
7.7　Neural Network Approach for the Symmetric
Eigenvalue Problem
7.8　Singular Value Decomposition
7.9　A Neurocomputing Approach for Solving the
Algebraic Lyapunov Equation
7.10　A Neurocomputing Approach for Solving the
Algebraic Riccati Equation
Problems
References
8　Solution of Linear Algebraic Equations Using Neural
Networks
8.1　Introduction
8.2　Systems of Simultaneous Linear Algebraic Equations
8.3　Least-Squares Solution of Systems of Linear Equations
8.4　A Least-Squares Neurocomputing Approach for
Solving Systems of Linear Equations
8.5　Conjugate Gradient Learning Rule for Solving
Systems of Linear Equations
8.6 A Generalized Robust Approach for Solving
Systems of Linear Equations Corrupted with Noise
8.7　Regularization Methods for Ill-Posed Problems with
Ill-Determined Numerical Rank
8.8　Matrix Splittings for Iterative Discrete-Time
Methods for Solving Linear Equations
8.9 Total Least-Squares problem
8.10 An L-Norm (Minimax) Neural Network for
Solving Linear Equations
8.11 An L1-Norm (Least-Absolute-Deviations) Neural
Network for Solving Linear Equations
Problems
References
9　Statistical Methods Using Neural Networks
9.1　Introduction
9.2　Principal-Component Analysis
9.3　Learning Algorithms for Neural Network Adaptive
Estimation of Principal Components
9.4 Principal-Component Regression
9.5 Partial Least-Squares Regression
9.6 A Neural Network Approach for Partial
Least-Squares Regression
9.7　Robust PLSR: A Neural Network Approach
Problems
References
10　Identification, Control, and Estimation Using Neural
Networks
10.1 Introduction
10.2 Linear System Representation
10.3　Autoregressive Moving Average Models
10.4　Identification of Linear Systems with ARMA Models
10.5　Parametric System Identification of Linear Systems
Using PLSNET
10.6　Nonlinear System Representation
10.7　Identification and Control of Nonlinear Dynamical
Systems
10.8　Independent-Component Analysis: Blind Separation
of Unknown Source Signals
10.9　Spectrum Estimation of Sinusoids in Additive Noise
10.10　Other Case Studies
Problems
References
App A　Mathematical Foundation for Neurocomputing
A.1 Introduction
A.2 Linear Algebra
A.3　Principles of Multivariable Analysis
A.4　Lyapunov's Direct Method
A.5　Unconstrained Optimization Methods
A.6　Constrained Nonlinear Programming
A.7　Random Variables and Stochastic Processes
A.8　Fuzzy Set Theory
A.9　Selected Trigonometric Identities
References
Name Index
Subject Index