Table of Contents
Preface v
1 Dynamic fuzzy machine learning model 1
1.1 Problem statement 1
1.2 DFMLmodel 1
1.2.1 Basic concept of DFMLs 2
1.2.2 DFM Lalgorithm 4
1.2.3 DFML geometric model description 13
1.2.4 Simulation examples 14
1.3 Relative algorithm of DFMLS 16
1.3.1 Parameter learning algorithm for DFMLS 16
1.3.2 Maximum likelihood estimation algorithm in DFMLS 21
1.4 Process control model of DFMLS 29
1.4.1 Process control model of DFMLS 29
1.4.2 Stability analysis 30
1.4.3 Design of dynamic fuzzy learning controller 34
1.4.4 Simulation examples 36
1.5 Dynamic fuzzy relational learning algorithm 39
1.5.1 An outline of relational learning 40
1.5.2 Problem introduction 43
1.5.3 DFRL algorithm 44
1.5.4 Algorithm analysis 47
1.6 Summary 48
References 48
2 Dynamic fuzzy autonomic learning subspace algorithm 51
2.1 Research status of autonomic learning 51
2.2 Theoretical system of autonomous learning subspace based on DFL 54
2.2.1 Characteristics of AL 54
2.2.2 Axiom system of AL subspace 56
2.3 Algorithm of ALSS based on DFL 57
2.3.1 Preparation of algorithm 58
2.3.2 Algorithm of ALSS based on DFL 60
2.3.3 Case analysis 63
2.4 Summary 66
References 66
3 Dynamic fuzzy decision tree learning 69
3.1 Research status of decision trees 69
3.1.1 Overseas research status 69
3.1.2 Domestic research status 70
3.2 Decision tree methods for a dynamic fuzzy lattice 72
3.2.1 ID3 algorithm and examples 72
3.2.2 Characteristics of dynamic fuzzy analysis of decision trees 74
3.2.3 Representation methods for dynamic fuzzy problems in decision trees 74
3.2.4 DFDT classification attribute selection algorithm 77
3.2.5 Dynamic fuzzy binary decision tree 82
3.3 DFDT special attribute processing technique 86
3.3.1 Classification of attributes 87
3.3.2 Process used for enumerated attributes by DFDT 87
3.3.3 Process used for numeric attributes by DFDT 88
3.3.4 Methods to process missing value attributes in DFDT 94
3.4 Pruning strategy of DFDT 98
3.4.1 Reasons for pruning 98
3.4.2 Methods of pruning 100
3.4.3 DFDT pruning strategy 101
3.5 Application 104
3.5.1 Comparison of algorithm execution 104
3.5.2 Comparison of training accuracy 105
3.5.3 Comprehensibility comparisons 109
3.6 Summary 110
References 110
4 Concept learning based on dynamic fuzzy sets 115
4.1 Relationship between dynamic fuzzy sets and concept learning 115
4.2 Representation model of dynamic fuzzy concepts 115
4.3 DF concept learning space model 117
4.3.1 Order model of DF concept learning 117
4.3.2 DF concept learning calculation model 120
4.3.3 Dimensionality reduction model of DF instances 125
4.3.4 Dimensionality reduction model of DF attribute space 126
4.4 Concept learning model based on DF lattice 129
4.4.1 Construction of classical concept lattice 129
4.4.2 Constructing lattice algorithm based on DFS 132
4.4.3 DF Concept Lattice Reduction 135
4.4.4 Extraction of DF concept rules 137
4.4.5 Examples of algorithms and experimental analysis 139
4.5 Concept learning model based on DFDT 142
4.5.1 DF concept tree and generating strategy 142
4.5.2 Generation of DF Concepts 143
4.5.3 DF concept rule extraction and matching algorithm 151
4.6 Application examples and analysis 152
4.6.1 Face recognition experiment based on DF concept lattice 152
4.6.2 Data classification experiments on UCI datasets 156
4.7 Summary 159
References 159
5 Semi-supervised multi-task learning based on dynamic fuzzy sets 161
5.1 Introduction 161
5.1.1 Review of semi-supervised multi-task learning 161
5.1.2 Problem statement 166
5.2 Semi-supervised multi-task learning model 167
5.2.1 Semi-supervised learning 167
5.2.2 Multi-task learning 172
5.3 Semi-supervised multi-task learning model based on DFS 178
5.3.1 Dynamic fuzzy machine learning model 179
5.3.2 Dynamic fuzzy semi-supervised learning model 180
5.3.3 DFSSMTL model 180
5.4 Dynamic fuzzy semi-supervised multi-task matching algorithm 182
5.4.1 Dynamic fuzzy random probability 183
5.4.2 Dynamic fuzzy semi-supervised multi-task matching algorithm 184
5.4.3 Case analysis 189
5.5 DFSSMTAL algorithm 192
5.5.1 Mahalanobis distance metric 192
5.5.2 Dynamic fuzzy K-nearest neighbour algorithm 193
5.5.3 Dynamic fuzzy semi-supervised adaptive learning algorithm 196
5.6 Summary 205
References 206
6 Dynamic fuzzy hierarchical relationships 209
6.1 Introduction 209
6.1.1 Research progress of relationship learning 209
6.1.2 Questions proposed 214
6.1.3 Chapter structure 215
6.2 Inductive logic programming 215
6.3 Dynamic fuzzy HRL 217
6.3.1 DFL relation learning algorithm (DFLR) 217
6.3.2 Sample analysis 222
6.3.3 Dynamic fuzzy matrix HRL algorithm 226
6.3.4 Sample analysis 232
6.4 Dynamic fuzzy tree hierarchical relation learning 235
6.4.1 Dynamic fuzzy tree 235
6.4.2 Dynamic fuzzy tree hierarchy relationship learning algorithm 238
6.4.3 Sample analysis 246
6.5 Dynamic fuzzy graph hierarchical relationship learning 249
6.5.1 Basic concept of dynamic fuzzy graph 249
6.5.2 Dynamic fuzzy graph hierarchical relationship learning algorithm 253
6.5.3 Sample analysis 255
6.6 Sample application and analysis 255
6.6.1 Question description 256
6.6.2 Sample analysis 260
6.7 Summary 262
References 262
7 Multi-agent learning model based on dynamic fuzzy logic 267
7.1 Introduction 267
7.1.1 Strategic classification of the agent learning method 267
7.1.2 Characteristics of agent learning 267
7.1.3 Related work 268
7.2 Agent mental model based on DFL 269
7.2.1 Model structure 269
7.2.2 Related axioms 274
7.2.3 Working mechanism 275
7.3 Single-agent learning algorithm based on DFL 277
7.3.1 Learning task 277
7.3.2 Immediate return single-agent learning algorithm based on DFL 277
7.3.3 Q-learning function based on DFL 279
7.3.4 Q-learning algorithm based on DFL 280
7.4 Multi-agent learning algorithm based on DFL 282
7.4.1 Multi-agent learning model based on DFL 282
7.4.2 Cooperative multi-agent learning algorithm based on DFL 282
7.4.3 Competitive multi-agent learning algorithm based on DFL 298
7.5 Summary 299
References 299
8 Appendix 301
8.1 Dynamic fuzzy sets 301
8.1.1 Definition of dynamic fuzzy sets 301
8.1.2 Operation of dynamic fuzzy sets 301
8.1.3 Cut set of dynamic fuzzy sets 304
8.1.4 Dynamic fuzzy sets decomposition theorem 305
8.2 Dynamic fuzzy relations 308
8.2.1 The conception dynamic fuzzy relations 308
8.2.2 Property of dynamic fuzzy relations 309
8.2.3 Dynamic fuzzy matrix 310
8.3 Dynamic fuzzy logic 312
8.3.1 Dynamic fuzzy Boolean variable 312
8.3.2 DF proposition logic formation 313
8.4 Dynamic fuzzy lattice and its property 316
Index 321
