Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More

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Springer Science & Business Media, Jan 1, 2008 - Computers - 256 pages
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This book was originally titled “Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms.” I have changed the subtitle to better represent the contents of the book. The basic philosophy of the original version has been kept in the new edition. That is, the book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Our goal is not to cover every latest progress in the fields, nor to discuss every detail of various techniques that have been developed. New sections/subsections added in this edition are: Simulated Annealing (Section 3.7), Boltzmann Machines (Section 3.8) and Extended Fuzzy if-then Rules Tables (Sub-section 5.5.3). Also, numerous changes and typographical corrections have been made throughout the manuscript. The Preface to the first edition follows. General scope of the book Artificial intelligence (AI) as a field has undergone rapid growth in diversification and practicality. For the past few decades, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have been added to the traditional symbolic AI. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. The newer fields include neural networks, genetic algorithms or evolutionary computing, fuzzy systems, rough set theory, and chaotic systems.
 

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The chapters on neural network (Ist), fuuzy and chaos make it a good read.

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Contents

Introduction
1
12 An Overview of the Areas Covered in this Book
3
2 Neural Networks
7
23 Basic Idea of the Backpropagation Model
8
24 Details of the Backpropagation Model
15
25 A Cookbook Recipe to Implement the Backpropagation Model
22
26 Additional Technical Remarks on the Backpropagation Model
24
27 Simple Perceptrons
28
524 Operations Unique to Fuzzy Sets
128
53 Fuzzy Relations
130
532 Fuzzy Relations Defined on Ordinary Sets Fuzzy relations
133
533 Fuzzy Relations Derived from Fuzzy Sets
138
542 Fuzzy Logic Fundamentals
139
55 Fuzzy Control
143
Controlling Temperature with a Variable Heat Source
150
553 Extended Fuzzy ifthen Rules Tables
152

28 Applications of the Backpropagation Model
31
29 General Remarks on Neural Networks
33
Neural Networks Other Models
37
32 Associative Memory
40
33 Hopfield Networks
41
The Basics
46
342 TwoDimensional Layout
48
Applications
49
352 A General guideline to apply the HopfieldTank model to optimization problems
54
353 Traveling Salesman Problem TSP
55
36 The Kohonen Model
58
37 Simulated Annealing
63
38 Boltzmann Machines
69
The Basics Architecture
70
Algorithms
76
384 Appendix Derivation of DeltaWeights
81
Genetic Algorithms and Evolutionary Computing
85
42 Fundamentals of Genetic Algorithms
87
43 A Simple Illustration of Genetic Algorithms
90
InputtoOutput Mapping
95
the Traveling Salesman Problem TSP
102
46 Schemata
108
461 Changes of Schemata Over Generations
109
462 Example of Schema Processing
113
47 Genetic Programming
116
48 Additional Remarks
118
Fuzzy Systems
121
52 Fundamentals of Fuzzy Sets
123
522 Basic Fuzzy Set Relations
125
523 Basic Fuzzy Set Operations and Their Properties
126
554 A Note on Fuzzy Control Expert Systems
155
56 Hybrid Systems
156
57 Fundamental Issues
157
58 Additional Remarks
158
Rough Sets
162
62 Review of Ordinary Sets and Relations
165
63 Information Tables and Attributes
167
64 Approximation Spaces
170
65 Knowledge Representation Systems
176
66 More on the Basics of Rough Sets
180
67 Additional Remarks
188
68 Case Study and Comparisons with Other Techniques
191
681 Rough Sets Applied to the Case Study Case study process control
192
682 ID3 Approach and the Case Study
195
683 Comparisons with Other Techniques
202
Chaos
206
72 Representing Dynamical Systems
210
722 Continuous dynamical systems
212
73 State and Phase Spaces
218
732 Cobwebs
221
74 Equilibrium Solutions and Stability
222
75 Attractors
227
751 Fixedpoint attractors
228
754 Chaotic attractors
233
76 Bifurcations
234
77 Fractals
238
78 Applications of Chaos
242
Index
247
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Munakata-Cleveland State University, OH

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