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《仿生模式识别与多权值神经元网络》一书包括三个部分：1）统计模式识别回顾；2）仿生模式识别；3）多权值神经元及神经网络。全书系统地介绍了传统的模式识别方法、仿生模式识别方法以及采用多权值神经网络的实现技术。该书是作者在智能科学领域多年从事系统研究基础上撰写的在理论上有重要意义的学术著作。体现出作者在神经网络、模式识别、人工智能等方面具有系统性和原创性的优异成果。该书采用图形方式来介绍一些基本概念以及
详细介绍了所提出的理论和方法在图像处理中和模式识别中的应用效果，是本书的突出特色。该书结构合理，内容丰富。
目录
Part Ⅰ Review of Statistics Pattern Recognition1
Chapter 1 Introduction of Pattern Recognition2
1.1 Patttern Recognition Concept3
1.2 Pattern Recognition System Biasic Processes4
1.3 A Brief Survey of Pattern Recognition Appro aches8
1.4 Scope and Organization10
Chapter 2 Kernel of Statistical Pattern Recognition And Pre-Precessing12
2.1 Question Arise12
2.1.1 Question Expression12
2.1.2 Empirical Risk Minimization13
2.1.3 Generalization Ability and Complexity14
2.2 Kernel of Statistical Pattern Recognition15
2.2.1 Vapnik-Chervonenkis Dimension15
2.2.2 The Bounds of Generalization Ability16
2.2.3 The Minimization of Structural Risk17
2.3 Preprocessing19
2.4 Feature Extraction and Feature Selection20
2.4.1 Curse of Dimensionality22
2.4.2 Feature Extraction22
2.4.3 Feature Selection30
2.5 Support Vector Manchine34
2.5.1 The Optimal Hyperplane Under Linearly Separable35
2.5.2 The Soft Spacing Under Linearly Nonseparable37
2.5.3 The Kernel Function Under Non-Linear Case38
2.5.4 Support Vector Machine’s Traits and Advantages39
References41
Part Ⅱ BIOMIMETIC PATTERN RECOGNITION43
Chapter 3 Introduction44
Chapter 4 The Foundation of Biomimetic Pattern Recognition46
4.1 Overview of High-Dimensional Biomimetic Informatics47
4.1.1 The Proposal of the Problem of Computer Imaginal Thinking47
4.1.2 The Principle of High-Dimensional Biomimetic Informatics47
4.2 Basic Contents of High-Dimensional Biomimetic Informatics48
4.3 Main Features of High-Dimensional Biomimetic Informatics49
4.4 Concepts and Mathematical Symbols In High-Dimensional Biomimetic Informatics51
4.4.1 Concepts and Definitions51
4.4.2 Mathematical Symbols55
4.4.3 Symbolic Computing Methods in Resolving Geometry Computing Problems56
4.4.4 Several Applications in Solving Complicated Geometry Computing Problems60
4.5 Some Applications63
4.5.1 Blurred Image Restoration63
4.5.2 Uneven Lighting Image Correction64
4.5.3 Removing Facial Makeup Disturbances65
Chapter 5 The Theory of Biomimetic Pattern Recognition68
5.1 The Concept of Biomimetic Pattern Recognition68
5.2 The Cholce of The Name69
5.3 The Developments of Biomimetic Pattern Recognition71
5.4 Covering：The Concept of Recognition in Biomimetic Pattern Recognition74
5.5 The Principle of Homology-Continuity：The Starting Point of Biomimetic Pattern Recognition76
5.6 Expansionary Product77
5.7 Experiments78
5.7.1 The Architecture of the Face Recognition System79
5.7.2 Umist Face Data80
5.7.3 Pre-treatment80
5.7.4 The Realization of SVM Face Recognition Algorithms80
5.7.5 The Realization of BPR Face Recognition Algorithms81
5.7.6 Experiments Results and Analyzes83
5.8 Summary86
Chapter 6 Applications88
6.1 Object Recognition88
6.2 A Multl-Camera Human-Face Personal Identification System93
6.3 A Recognition System For Speaker-Independent Continuous Speech98
6.4 Summary105
References106
Part Ⅲ Multi-Weight Neurons And Networks109
Chapter 7 History and Definations of Artificial Neural Networks110
7.1 From Biological Neural Networks to Artificial Neural Networks and Its Development110
7.2 Some Definitions and Concepts of Artificial Neural Networks114
7.3 Unifications and Divergences Between Array-Processors and Neural Networks119
7.4 Artificial Neural Networks’ Effects on Nanoelectronical Computational Technology124
Chapter 8 Geometric Concepts of artificial Neurons128
8.1 Mathematical Expressions of Common Neurons and Their Geometric Concepts128
8.2 General Mathematical Model of Common Neurons and Its Geometric Concept131
8.3 Direction Basis Function Neuron and Its Geometric Concept135
8.4 Multi-Threshold Neurons and Networks138
Chapter 9 Multi-Weight Neurons and Their Applications144
9.1 General Mathematical Expression of Multi-Weight Neurons’ Functions144
9.2 Interchangeabilities of Points，Vectors，Hyper Planes in High-Dimensional Space145
9.3 Effect of High-Dimensional Point Distribution Analysis in Information Technology148
9.4 Multi-Weight Neurons are Computing Tools on High-Dimensional Point Distribution Analysis153
9.5 Applications of Multi-Weight Neurons and Networks on Biomimetic Pattern Recognition156
References163
Appendix Experts’ Evaluation to The Book179
