神经网络设计(英文影印版)

内容简介

本书清楚而详细地论述了基本的神经网络体系结构和训练方法、作者重点调了三项内容： 一是神经网络的数学分析，二是神经网络的训练方法 三是神经网络的工程应用——主要是在模式识别、信号处理和控制系统领域的应用。 本书特点： ·广泛论述了能力学习方面的内容，包括Widrow-Hoff规则、反向传播算法和一些增强的反向传播算这（例如， 变梯度法，Levenberg-Marquardt动量项法） ·讨论了回归互联记忆神经网络(例如．Hopfield神经网络) ·给出多个解决问题的详细实例： ·以简单的积木形式解释了互联神经网络和竞争神经网络(包括特征映射、学习矢量量化和自适应共振理论)。 ·提供了用MATLAB4．O实现的神经网络设计演示程序（包含学生版和专业版） 这是一本的著作 很难见到写得这么好的书。本书无论是插图还是范例都是的这些插图和范例不但丰富了内容，而且还增加了直觉感。

目录

Preface 1.?Introduction Objectives History Applications Biological?Inspiration Further?Reading 2.?Neuron?Model?and?Network?Architectures Objectives Theory?and?Examples Notation Neuron?Model Single-Input?Neuron Transfer?Functions Multiple-Input?Neuron Network?Architectures A?Layer?of?Neurons Multiple?Layers?of?Neurons Recrrent?Networks Summary?of?Results Solved?Problems Epilogue Exercises 3.?An?Illustrative?Example Objectives Theory?and?Examples Problem?Statement Perceptron Two-Input?Case Pattern?Recognition?Example Hamming?Network Feedforward?Layer Recurrent?Layer Hopfield?Network Epilogue Exercise 4.?Perceptron?Learning?Rule Objectives Theory?and?Examples Learning?Rules Perceptron?Architecture Single-Neuron?Perceptron Multiple-Neuron?Perceptron Perceptron?Learning?Rule Test?Problem Constructing?Learning?Rules Unified?Learning?Rule Training?Multiple-Neuron?Perceptrons Proof?of?Convergence Notation Proof Limitations Summary?of?Results Solved?Problems Epilogue Further?Reading Exercises 5.?Signal?and?Weight?Vector?Spaces Objectives Theory?and?Examples Linear?Vector?Spaces Linear?independence Spanning?a?Space Inner?Product Norm Orthogonality Gram-Schmidt?Orthogonalization Vector?Expansions Reciprocal?Basis?Vectors Summary?of?Results Solved?Problems Epilogue Further?Reading Exercises 6.?Linear?Transformations?for?Neural?Networks Objectives Theory?and?Examples Linear?Transformations Matrix?Representations Change?of?Basis Eigenvalues?and?Eigenvectors Diagonalization Summary?of?Results Solved?Problems Epilogue Further?Reading Exercises 7.?Supervised?Hebbian?Learning Objectives Theory?and?Examples Linear?Associator The?Hebb?Rule Performance?Analysis Pseudoinverse?Rule Application Variations?of?Hebbian?Learning Summary?of?Results Solved?Problems Epilogue Further?Reading Exercises 8.?Performance?Surfaces?and?Optimum?Points Objectives Theory?and?Examples Taylor?Series Vector?Case Directional?Derivatives Minima Necessary?Conditions?for?Optimality First-Order?Conditions Second-Order?Conditions Quadratic?Functions Eigensystem?of?the?Hessian Summary?of?Results Solved?Problems Epilogue Further?Reading Exercises 9.?Performance?Optimization Objectives Theory?and?Examples Steepest?Descent Stable?Learning?Rates Minimizing?Along?a?Line Newton''s?Method Conjugate?Gradient Summary?of?Results Solved?Problems Epilogue Further?Reading Exercises 10.?Widrow-Hoff?Learning Objectives Theory?and?Examples ADALINE?Network Single?ADALINE Mean?Square?Error LMS?Algorithm Analysis?of?Convergence Adaptive?Filtering Adaptive?Noise?Cancellation Echo?Cancellation Summary?of?Results Solved?Problems Epilogue Further?Reading Exercises 11.?Backpropagation Objectives Theory?and?Examples Multilayer?Perceptrons Pattern?Classification?'' Function?Approximation The?Backpropagation?Algorithm Perfo