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人类的头脑约由1011个神经元所组成，所有的讯息就在神经元与神经元间靠着轴突及树突的发送与接收来传递。在这样的一个过程中，所接收进来的各种讯息被分类或辨认，进而形成了人类的认知与思维。现在我们利用数学的计算来模拟神经元的运作，进而模拟神经网路的传送，以期达到分类或辨认。类神经网路的特点为学习，学习的目的是要调整神经腱的大小，即调整加权系数，我们要探讨各种就是学习法则。的类神经网路的模型及其加权系数的调整公式，也本书着重于利用类神经网路的方法于图形辨识与最佳化问题之解决，因此将先介绍传统的识别方法，再介绍类神经网路的各种理论及模型。本书提供基本的例子让读者容易了解，容易进入类神经网路的领域，在探讨的多个模型中，均有自己提出的见解。

本书特色

　　1.本书着重于利用类神经网路的方法于模式辨别与最佳化问题之解决。

　　2.提供基础范例让读者容易了解，容易进入类神经网路的领域。

　　3.在何普菲模型应用于解销售员旅行问题(TSP)走最短距离的回旋距离的优化，有详细的分析；在何普菲类神经网路及一般化的蜂窝神经网络也有做基本的介绍。

第一章 简介
1.1 图型的定义与图型识别的方法
1.2 Decision-theoretic Approach的图形识别与空间分割
1.3 Pattern Recognition Systems
1.4 Non-parametric & Parametric Methods
1.5 人类头脑的Neuron与模拟的Perceptron
1.6 Two Class Data分佈的复杂性
1.7 Activation Function
1.8 Development History of Neural Networks
1.9 Neural Network Applications

第二章 DECISION-THEORETIC PATTERN RECOGNITION 决策理论的图形识别Decision-theoretic Approach的图形识别与Discriminant Functions
2.2 Nonparametric Pattern Recognition非参数式之图形识别：Using Discriminant Functions
2.2.1 Linear discriminant functions for pattern recognition
2.2.2 Nonlinear discriminant functions for pattern recognition
2.2.3 Perpendicular bisector
2.2.4 Minimum-distance classifier
2.2.5 Minimum-distance classifier with respect to point sets (Piecewise-linear discriminant functions, Nearest-neighbor classification)
2.2.6 N-nearest neighbor classification rule
2.3 Parametric Pattern Recognition 参数式之图形识别
2.3.1 Bayes theorem (贝氏定理) and probability density function (pdf)
2.3.2 Bayes (Parametric) classification rule (贝氏分类法则)
2.3.3 Sequential classification
2.3.4 Neyman-Pearson test
2.3.5 Linear Classifier Design
2.3.6 Feature selection
2.3.7 Error estimation
2.4 Unsupervised Pattern Recognition
2.4.1 Minimum spanning tree (MST) clustering
2.4.2 K-means clustering
2.4.3 Hierarchical Clustering Using Dendrogram (Unsupervised Clustering) 2

第三章 PERCEPTRON 认知器数学上解Decision Boundary之困难
3.2 Perceptron
3.3 Classification
3.4 Training (Learning)
3.5 Flowcharts of Perceptron
3.6 Convergence Proof of Perceptron for Fixed Increment Training Procedure
3.7 Perceptron for Logic Operation
3.8 Layered Machine (Committee Machine/Voting Machine)
3.9 Multiclass Perceptrons
3.10 Perceptron with Sigmoidal Activation Function and Learning by Gradient Descent Method
3.11 Modified Fixed-increment Training Procedure
3.12 Multiclass Perceptron with Delta Learning Rule
3.13 Widrow-Hoff Learning Rule
3.14 Correlation Learning Rule

第四章 MULTILAYER PERCEPTRON 多层认知器 Introduction
4.2 设计Multilayer Perceptron with 1 Hidden Layer 解XOR的分类问题
4.3 Gradient and Gradient Descent Method in Optimization
4.4 Multilayer Perceptron (MLP) and Forward Computation
4.5 Back-propagation Learning Rule (BP)
4.5.1 Analysis
4.5.2 Back-propagation learning algorithm of one-hidden layer perceptron (I)
4.5.3 Back-propagation learning algorithm of one-hidden layer perceptron (II)
4.6 Experiment of XOR Classification & Discussions
4.7 On Hidden Nodes for Neural Nets
4.8 Application - NETtalk：A Parallel Network That Learns to Read Aloud
4.9 Functional-Link Net

第五章 RADIAL BASIS FUNCTION NETWORK (RBF) 辐射基底函数网路 Introduction
5.2 RBF Network 第一层的Learning Algorithm
5.3 RBF Network 第二层的Learning Algorithm
5.4 设计RBF Model to Classify XOR Patterns

第六章 SUPPORT VECTOR MACHINE (SVM) 支持向量的分类器Introduction
6.2 点到Hyperplane之距离
6.3 Role of Support Vectors in Optimal Margin Classifier for Linearly Separable Case
6.4 Find Optimal Margin Classifier for Linearly Separable Case
6.5 SVM for Nonseparable Patterns
6.5.1 Primal Problem
6.5.2 Dual Problem
6.6 Feature Transformation and Support Vector Machine (SVM) – Kernel SVM
6.6.1 Primal Problem and Optimal Separating Hyperplane之建立
6.6.2 在Dual Problem上求解新的Feature Space上的Support Vector Machine
6.6.3 Gradient Ascent的调适性的方法求 Lagrange Multipliers
6.7 Multiclss Classification Using Support Vector Machine
6.7.1 Maximum Selection Classification System Using SVMs
6.7.2 利用SVM 于数字辨识的树状分类系统 (Tree Classification System)
6.7.3 Multi-class Classification Using Many Binary SVMs
6.8 SVM Examples
6.8.1 直接利用Lagrange method (没有利用KKT conditions 的Lagrange method)
6.8.2 利用加入KKT 的Lagrange method
6.8.3 Support Vector Machine (SVM) Using Feature Transformation – Kernel SVM
6.8 Exercise

第七章 KOHONEN’S SELF-ORGANIZING NEURAL NET 自我组织的类神经网路 Winner-Take-All Learning Rule
7.2 Kohonen’s Self-organizing Feature Maps
7.3 Self-organizing Feature Maps于TSP

第八章 PRINCIPAL COMPONENT NEURAL NET 主分量类神经网路Introduction
8.2 Hebbian Learning Rule
8.3 Oja的学习法则
8.4 Neural Network of Generalized Hebbian Learning Rule
8.5 Data Compression
8.6 Effect of Adding One Extra Point along the Direction of Existing
Eigenvector
8.7 Neural network的PCA的应用

第九章 HOPFIELD NEURAL NET
9.1 Lyapunov Function
9.2 Discrete Hopfield Model
9.3 Analog Hopfield Model
9.3.1 Circuits and Power
9.3.2 Analog Hopfield Model
9.4 Optimization Application of Hopfield Model to TSP
9.5 与Hopfield Neural Net有关的研究与应用

第十章 CELLULAR NEURAL NETWORK 蜂巢式类神经网路
10.1 简介
10.2 蜂巢式类神经网路架构
10.3 蜂巢式类神经网路的稳定性分析
10.4 蜂巢式类神经网路与Hopfield神经网路的比较
10.5 离散蜂巢式类神经网路

第十一章 HAMMING NET
11.1 Introduction
11.2 Hamming Distance and Matching Score
11.3 Hamming Net Algorithm
11.4 Comparator

第十二章 ADAPTIVE RESONANCE THEORY NET (ART)
12.1 Introduction
12.2 ART1 Neural Model
12.3 Carpenter/Grossberg ART1 Net的Algorithm
12.4 Revised ART algorithm

第十三章 FUZZY, CLUSTERING, AND NEURAL NETWORKS
13.1 Fuzzy C-means Clustering Algorithm
13.2 Fuzzy Perceptron
13.3 Pocket Learning Algorithm
13.4 Fuzzy Pocket

参考文献

附录
Appendix A：Inner Product (内积)
Appendix B：Line Property and Distance from Point to Line
Appendix C：Covariance Matrix
Appendix D：Gram–Schmidt Orthonormal Procedure
Appendix E：Lagrange Multipliers Method
Appendix F：Gradient, Gradient Descent and Ascent Methods in Optimization
Appendix G：Derivation of Oja’s learning rule
Appendix H：类神经网路程式实验报告范例
Appendix I：实验报告范例之电脑程式
Appendix J：MATLAB Program of Perceptron
Appendix K：MATLAB Program of Multilayer Perceptron
Appendix L：FORTRAN Program for Perceptron
Appendix M：画aX+bY+cZ+常数= 0的平面的Matlab电脑程式
Appendix N：Support Vector Machine的数学推导
Appendix O：Projects
Appendix P：Project #1的部份Matlab程式

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类神经网路(第四版)(附范例光碟) 电子书 下载 mobi epub pdf txt 2024

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