人工神经网络理论及应用(英文版)/高等学校人工智能教育丛书
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北京朝阳
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作者文常保
出版社西安电子科技大学出版社有限公司
ISBN9787560660356
出版时间2020-06
装帧平装
开本16开
定价55元
货号11215406
上书时间2024-06-27
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目录
Section 1 Foundation of neural network
Chapter 1 Theoretical basis of biological neural network 2
1.1 Structure and function of biological neurons 2
1.2 Electrical activity of the nervous system 5
1.3 Information storage of human brain 9
1.4 Human brain and computer 11
Exercises 16
References 17
Chapter 2 Review of artificial neural network 18
2.1 Development history of artificial neural network 18
2.2 Characteristics of artificial neural network 28
2.3 Applications of artificial neural network 30
Exercises 38
References 39
Chapter 3 Mathematical basis of artificial neural network 40
3.1 Neuron model 40
3.1.1 Symbol description 40
3.1.2 Single input neuron 41
3.1.3 Transfer function 41
3.1.4 Multiple input neurons 45
3.2 Derivatives 45
3.3 Differential 47
3.4 Integrals 47
3.5 Gradient 48
3.6 Determinant 49
3.7 Matrices 50
3.7.1 Concept 50
3.7.2 Operation of matrices 51
3.7.3 Operational properties of matrices 51
3.8 Vector 52
3.9 Eigenvalues and eigenvectors 53
3.10 Random events and probabilities 53
3.11 Norm 55
Exercises 57
References 58
Section 2 Theory of artificial neural network
Chapter 4 Perceptrons 60
4.1 Introduction 60
4.2 Architecture and principle of perceptron 61
4.2.1 Architecture of perceptron 61
4.2.2 Principle of perceptron 62
4.2.3 Learning strategies of perceptron 64
4.3 Single layer perceptron 65
4.3.1 Single layer perceptron model 65
4.3.2 Function of single layer perceptron 67
4.3.3 Learning algorithm of single layer perceptron 69
4.3.4 Limitations of single layer perceptron 73
4.4 Multilayer perceptron 74
4.4.1 Architecture and principle of multilayer perceptron 74
4.4.2 Functions of multilayer perceptron 75
4.4.3 Multilayer perceptron learning algorithm 78
4.5 Applications 79
4.5.1 Case Ⅰ 79
4.5.2 Case Ⅱ 81
Exercises 85
References 86
Chapter 5 Back Propagation neural network 87
5.1 Introduction 87
5.2 BP neural network architecture 89
5.3 BP algorithm 90
5.3.1 Algorithmic principles 90
5.3.2 Back propagation examples 95
5.4 Shortcomings and improvement of BP algorithm 98
5.4.1 Shortcomings of BP algorithm 98
5.4.2 BP algorithm improvement 102
5.5 Applications 105
5.5.1 Case Ⅰ 105
5.5.2 Case Ⅱ 108
5.5.3 Case Ⅲ 110
Exercises 113
References 114
Chapter 6 RBF neural network 115
6.1 Introduction 115
6.2 Architecture and principle of RBF neural network 116
6.2.1 RBF neuron model 116
6.2.2 RBF neural network architecture 117
6.2.3 Principles of RBF neural network 118
6.3 RBF neural network algorithm 119
6.4 Related problems of RBF neural network 122
6.5 Applications 123
6.5.1 CaseⅠ 123
6.5.2 CaseⅡ 125
Exercises 126
References 127
Chapter 7 Adaline neural network 128
7.1 Introduction 128
7.2 Architecture and principles of Adline 129
7.2.1 Single layer Adaline model 129
7.2.2 Algorithm and principles 130
7.2.3 Multilayer Adaline model 133
7.3 Applications 136
7.3.1 Case Ⅰ 136
7.3.2 Case Ⅱ 138
Exercises 141
References 142
Chapter 8 Hopfield neural network 143
8.1 Introduction 143
8.2 Discrete Hopfield neural network 144
8.2.1 Network architecture 144
8.2.2 Working principles 145
8.2.3 Network stability 146
8.2.4 Network algorithm 148
8.3 Continuous Hopfield neural network 150
8.3.1 Network architecture 151
8.3.2 Network stability 153
8.4 Applications 153
8.4.1 Case Ⅰ 153
8.4.2 Case Ⅱ 156
Exercises 161
References 162
Chapter 9 Deep convolutional neural network 163
9.1 Introduction 163
9.2 Architecture and principle of deep convolution neural network 164
9.2.1 Architecture of deep convolutional neural network 164
9.2.2 Principle of deep convolutional neural network 166
9.3 Some basic deep convolutional neural networks 168
9.3.1 AlexNet 168
9.3.2 VGGNet 168
9.3.3 ResNet 170
9.4 Applications 171
9.4.1 Several application frameworks of deep learning 171
9.4.2 Image recognition based on AlexNet 173
Exercises 177
References 177
Chapter 10 Generative adversarial networks 179
10.1 Introduction 179
10.2 Architecture of GAN 181
10.3 GAN algorithm 182
10.4 Improved GAN 185
10.4.1 DCGAN 185
10.4.2 SGAN 186
10.4.3 InfoGAN 187
10.4.4 CGAN 187
10.4.5 ACGAN 188
10.5 Applications 189
Exercises 191
References 192
Chapter 11 Elman neural network 193
11.1 Introduction 193
11.2 Architecture and principle of Elman neural network 193
11.2.1 Elman neural network architecture 193
11.2.2 Principle of Elman neural network 194
11.3 Learning algorithm of Elman neural network 196
11.4 Stability analysis of Elman neural network 198
11.5 Applications 200
11.5.1 Case Ⅰ 200
11.5.2 Case Ⅱ 203
Exercises 205
References 206
Chapter 12 AdaBoost neural network 207
12.1 Introduction 207
12.2 Architecture and algorithm of AdaBoost network 208
12.2.1 Architecture and principles 208
12.2.2 AdaBoost algorithm 209
12.3 Influence factors in AdaBoost algorithm 211
12.3.1 Training error analysis 211
12.3.2 Loss function in AdaBoost classification 212
12.3.3 Regularization of AdaBoost algorithm 214
12.4 Applications 215
Exercises 222
References 223
Chapter 13 SOFM neural network 224
13.1 Introduction 224
13.2 Architecture of SOFM neural network 225
13.3 Principle and algorithm of SOFM neural network 226
13.3.1 Principle of SOFM neural network 226
13.3.2 SOFM neural network learning algorithm 230
13.4 Applications 230
13.4.1 Case Ⅰ 230
13.4.2 Case Ⅱ 233
Exercises 237
References 238
Section 3 Design and practical application of artificial neural network
Chapter 14 Artificial neural network modeling based on Simulink 240
14.1 Introduction 240
14.2 Simulink startup and neural network module library 241
14.2.1 Startup of Simulink 241
14.2.2 Simulink neural network module library 243
14.3 Model setting and operation 247
14.3.1 Module operation 247
14.3.2 Operation of signal line 247
14.3.3 Setting of simulation parameters 248
14.3.4 Setting of common modules 250
14.4 Single neuron modeling 254
14.5 Simulink simulation model of function approximation
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