String-Rewriting Systems

Springer Science & Business Media
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The subject of this book is string-rewriting systems. It is generally accepted that string-rewriting was first introduced by Axel Thue in the early part of this century. In the 1960's and early 1970's, it received renewed attention due to interest in formal language theory. In the 1980's and 1990's, it has received more interest since it can be viewed as a special case of term rewriting, a subject that has become important in the study of automated deduction. Today, string-rewriting is studied by researchers in theoretical computer science and also by researchers interested in the foundations of artificial intelligence. A sketch of the way that the subject has developed is contained in Chapter 0, and the reader is advised to begin with that chapter. Both authors have been active in the field and have lectured on the subject in several universities. Lecture notes have been produced and dis tributed. This monograph is a result of revising and rewriting those notes. It represents an attempt by the authors to present the concepts that the authors consider to be most fundamental and to gather together the most useful results in such a way that they can be understood and used in studies relating to more general rewriting, to automated deduction, and to algo rithmic problems of algebraic structures. This monograph is written for independent study by researchers in the oretical computer science or in the foundations of artificial intelligence.
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Additional Information

Publisher
Springer Science & Business Media
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Published on
Dec 6, 2012
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Pages
189
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ISBN
9781461397717
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Language
English
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Genres
Computers / Information Technology
Computers / Intelligence (AI) & Semantics
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This content is DRM protected.
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63 3. 2 Function Level Adaptation 64 3. 3 Parameter Level Adaptation. 67 3. 4 Structure Level Adaptation 70 3. 4. 1 Neuron Generation . 70 3. 4. 2 Neuron Annihilation 72 3. 5 Implementation . . . . . 74 3. 6 An Illustrative Example 77 3. 7 Summary . . . . . . . . 79 4 Competitive Signal Clustering Networks 93 4. 1 Introduction. . 93 4. 2 Basic Structure 94 4. 3 Function Level Adaptation 96 4. 4 Parameter Level Adaptation . 101 4. 5 Structure Level Adaptation 104 4. 5. 1 Neuron Generation Process 107 4. 5. 2 Neuron Annihilation and Coalition Process 114 4. 5. 3 Structural Relation Adjustment. 116 4. 6 Implementation . . 119 4. 7 Simulation Results 122 4. 8 Summary . . . . . 134 5 Application Example: An Adaptive Neural Network Source Coder 135 5. 1 Introduction. . . . . . . . . . 135 5. 2 Vector Quantization Problem 136 5. 3 VQ Using Neural Network Paradigms 139 Vlll 5. 3. 1 Basic Properties . 140 5. 3. 2 Fast Codebook Search Procedure 141 5. 3. 3 Path Coding Method. . . . . . . 143 5. 3. 4 Performance Comparison . . . . 144 5. 3. 5 Adaptive SPAN Coder/Decoder 147 5. 4 Summary . . . . . . . . . . . . . . . . . 152 6 Conclusions 155 6. 1 Contributions 155 6. 2 Recommendations 157 A Mathematical Background 159 A. 1 Kolmogorov's Theorem . 160 A. 2 Networks with One Hidden Layer are Sufficient 161 B Fluctuated Distortion Measure 163 B. 1 Measure Construction . 163 B. 2 The Relation Between Fluctuation and Error 166 C SPAN Convergence Theory 171 C. 1 Asymptotic Value of Wi 172 C. 2 Energy Function . .
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