Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines

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Springer Science & Business Media, 2003 - Computers - 307 pages
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Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems.
  

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Contents

Prologue
1
1 Evolving Processes and Evolving Connectionist Systems
7
12 Working Classification Scheme for Learning in Connectionist Systems
12
13 Artificial Intelligence Al Versus Emerging Intelligence El
25
14 Introduction to Evolving Connectionist Systems
26
15 Summary and Open Problems
28
16 Further Reading
29
2 Evolving Connectionist Systems for Unsupervised Learning
31
82 Dynamic DNA and RNA Sequence Data Analysis and Knowledge Discovery
170
83 Gene Expression Data Analysis Rule Extraction and Disease Profiling1
174
84 Fuzzy Evolving Clustering of Genes According to Their TimeCourse Expression
184
85 Protein Structure Prediction
186
86 Dynamic Cell Modelling
189
87 Summary and Open Problems
191
9 Dynamic Modelling of Brain Functions and Cognitive Processes
193
92 Dynamic Modelling of Brain States Based on EEG Signals
197

22 ECOS for OnLine Clustering
39
23 SelfOrganising Maps SOMs
49
24 Evolving SelfOrganising Maps ESOM
52
25 Summary and Open Problems
55
3 Evolving Connectionist Systems for Supervised Learning
57
32 Evolving Fuzzy Neural Networks EFuNN
65
33 Knowledge Manipulation in Evolving Fuzzy Neural Networks EFuNNs Rule Insertion Rule Extraction Rule Aggregation
75
34 OnLine Evaluation Feature Modification and Parameter Adaptation in EFuNNs
85
35 Summary and Open Questions
88
4 Recurrent Evolving Systems Reinforcement Learning and Evolving Automata
91
42 Evolving Connectionist Systems and Evolving Automata
95
43 Reinforcement Learning in ECOS
96
44 Summary and Open Questions
97
45 Further Reading
98
5 Evolving NeuroFuzzy Inference Systems
99
52 Hybrid NeuroFuzzy Inference Systems HyFIS
104
53 Dynamic Evolving NeuroFuzzy Inference Systems DENFIS
107
54 Different Types of Fuzzy Rules in ECOS
116
55 Type2 Evolving Connectionist Systems
118
56 IntervalBased Evolving Connectionist Systems and Other Ways of Defining Receptive Fields
120
57 Summary and Open Problems
123
6 Evolutionary Computation and Evolving Connectionist Systems
125
62 Evolutionary Computation EC for the Optimisation of OffLine Learning Connectionist Systems
131
63 Evolutionary Computation for the Optimisation of OnLine Learning Systems
134
64 Summary and Open Problems
140
7 Evolving Connectionist Machines Framework Biological Motivation and Implementation Issues
143
72 Biological Motivation for ECOS the Instinct for Information
148
73 Spatial and Temporal Complexity of ECOS
150
74 OnLine Feature Selection and Feature Evaluation
151
75 An AgentBased Framework for Evolving Connectionist Machines
156
76 Evolving Hardware
158
77 Conclusion and Open Questions
160
8 Data Analysis Modelling and Knowledge Discovery in Bioinformatics
165
93 Dynamic Modelling of Cognitive Processes Based on Brain Imaging
200
94 Modelling Perception the Auditory System
202
95 Dynamic Modelling of Integrated Auditory and Visual Systems
204
96 Computational Models of the Entire Brain
206
97 Summary and Open Problems
207
98 Further Reading
208
10 Modelling the Emergence of Acoustic Segments Phonemes in Spoken Languages
209
102 The Dilemma of Innateness Versus Leaming or Nature Versus Nurture Revisited
211
103 ECOS for Modelling the Emergence of Phones and Phonemes
213
104 Modelling Evolving Bilingual Systems
221
105 Summary and Open Problems
225
106 Further Reading
227
11 OnLine Adaptive Speech Recognition
229
112 A Framework of Evolving Connectionist Systems for Adaptive Speech Recognition
232
114 OnLine Adaptive Whole Word and Phrases Recognition
237
115 Feature Selection and Feature Evaluation for OnLine Adaptive Speech Recognition Systems
240
116 Natural Language Understanding for Adaptive Intelligent Human Computer Interfaces
242
117 Summary and Open Problems
243
12 OnLine Image and Video Data Processing
245
122 OnLine Image Classification
248
123 OnLine Video Camera Operation Recognition
251
124 Summary and Open Problems
255
125 Further Reading
256
13 Evolving Systems for Integrated MultiModal Information Processing
257
a Framework for Integrated Auditory and Visual Information Processing Systems
258
133 PIAVI an Experimental Evolving System for Person Identification Based on Integrated Auditory and Visual Information Processing
260
134 Summary and Open Problems
270
135 Further Reading
271
Epilogue
273
References
275
Extended Glossary
291
Index
305
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