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Tables of Contents for Neuro-Fuzzy Pattern Recognition
Chapter/Section Title
Page #
Page Count
Foreword
xiii
Preface
xvii
List of Figures
xxi
Introduction
1
28
Introduction
1
3
Machine Recognition of Patterns
4
2
Preliminaries of Pattern Recognition
6
6
Data acquisition
7
1
Feature selection
7
1
Classification
8
3
Applications
11
1
Relevance of Fuzzy Set Theoretic Approach
12
2
Connectionist Approach: Relevance and Features
14
4
Need for Integrating Fuzzy Logic and Artificial Neural Networks
18
11
References
20
9
Fuzzy Logic and Neural Networks: Models, Integration, and Soft Computing
29
66
Introduction
29
1
Fuzzy Sets
30
8
Membership functions
32
1
Basic operations
33
1
Measures of fuzziness
34
2
T-norm and T - conorm
36
1
Fuzzy implication operators
37
1
Fuzzy Models for Pattern Recognition
38
4
Artificial Neural Networks
42
17
Single-layer perceptron
44
2
Multilayer perceptron (MLP) using backpropagation of error
46
3
Kohonen network
49
2
Radial basis function network
51
2
Hopfield network
53
2
Adaptive resonance theory (ART)
55
4
Neuro-Fuzzy Computing
59
7
Various ways of integration
59
3
Mathematical formulation of a fuzzy neural network
62
2
Neural implementation of possibility measure
64
1
Fuzzy and neural systems: universal approximators and equivalence
65
1
Knowledge-Based Networks
66
4
Generalization capability
67
2
Fuzzy knowledge-based networks
69
1
Soft Computing
70
25
Relevance
71
2
Different hybridizations
73
1
References
74
21
Pattern Classification
95
60
Introduction
95
2
Neuro-Fuzzy Models
97
11
Incorporating fuzziness in neural net framework
97
3
Designing neural net by fuzzy logic formalism
100
2
Changing basic characteristics of neurons
102
6
Measures of fuzziness as error of network
108
1
Fuzzy MLP
108
19
Pattern representation in linguistic form
109
3
Class memberships as output vectors
112
2
Weight updating
114
2
Results
116
11
Fuzzy Logical MLP
127
9
The model
128
1
Backpropagation algorithm based on logical operations
129
2
Results
131
5
Fuzzy Kohonen Network for Classification
136
19
Incorporating class information in input vector
138
1
The algorithm
139
4
Results
143
6
References
149
6
Other Applications of Fuzzy MLP
155
26
Introduction
155
1
Application to the Medical Domain
156
3
Hepatobiliary disorders
156
2
Kala-azar
158
1
Selective Partitioning of Feature Space
159
8
Algorithm
160
1
Results
161
6
Fingerprint Classification
167
14
Feature extraction
168
4
Fingerprint categories
172
1
Noisy pattern generation
172
2
Results
174
4
References
178
3
Self-Organization, Pixel Classification, and Object Extraction
181
32
Introduction
181
2
Self-Organizing Multilayer Neural Network with Fuzziness Measures
183
16
Description and operation of the network
183
3
Weight correction for different error (fuzziness) measures
186
4
Learning rate for different error (fuzziness) measures
190
3
Results
193
6
Cellular Network and Genetic Algorithms with Fuzziness Measures
199
14
Cellular neural networks
201
1
Object extraction using CNN
202
1
Selection of cloning template
203
2
Optimum selection of cloning template
205
1
Results
206
4
References
210
3
Feature Evaluation
213
46
Introduction
213
1
Supervised Feature Selection
214
23
Fuzzy evaluation index and weighted membership function
215
3
Connectionist realization
218
2
Theoretical analysis
220
7
Results
227
10
Unsupervised Feature Selection
237
8
Feature evaluation index
238
2
Connectionist minimization of E
240
3
Results
243
2
Unsupervised Feature Extraction
245
14
Connectionist model
246
4
Algorithm for learning α and W
250
1
Results
250
5
References
255
4
Rule Generation and Inferencing
259
50
Introduction
259
2
Neuro-Fuzzy Models
261
9
Connectionist (nonfuzzy) models
261
3
Incorporating fuzziness in neural net framework
264
1
Designing neural net by fuzzy logic formalism
265
1
Changing basic characteristics of neurons
266
4
Fuzzy MLP
270
19
Input representation
270
2
Forward pass
272
2
Querying
274
1
Justification
275
3
Results
278
11
Fuzzy Logical MLP
289
4
Forward pass
289
1
Querying
290
2
Justification
292
1
Results
292
1
Fuzzy Kohonen Network
293
16
Forward pass
295
2
Querying
297
1
Justification
297
2
Results
299
5
References
304
5
Using Knowledge-Based Networks and Fuzzy Sets
309
30
Introduction
309
1
Various Models
310
8
Knowledge-based networks
311
2
Incorporating fuzziness
313
4
Incorporating genetic algorithms
317
1
Classification with Knowledge-Based Fuzzy MLP
318
4
Knowledge encoding
318
2
Example
320
1
Pruning and growing
321
1
Rule Generation with Knowledge-Based Fuzzy MLP
322
5
Numeric and/or linguistic inputs: method 1
323
1
Backtracking along trained connection weights: method 2
324
3
Results
327
12
Classification
328
4
Rule generation
332
4
References
336
3
Rough--Fuzzy Knowledge-Based Networks
339
20
Introduction
339
1
Rough Set Characteristics
340
3
Knowledge Encoding Using Rough Sets
343
1
Configuration of Rough--Fuzzy MLP for Classification
344
4
Method 1
344
3
Method 2
347
1
Results
348
11
Method 1
348
3
Method 2
351
6
References
357
2
Appendix A Genetic Algorithms: Basic Principles, Features
359
4
References
363
2
Appendix B Derivation of the Expression for ε(E)
365
6
Index
371