Knowledge-Based Neurocomputing

A Fuzzy Logic Approach

By: Eyal Kolman, Michael Margaliot

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Hardcover | 17 January 2009

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118 Pages

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24.13 x 16.51 x 0.64

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In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB.

The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.

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Preface	p. VII
List of Abbreviations	p. IX
List of Symbols	p. XI
Introduction	p. 1
Artificial Neural Networks (ANNs)	p. 2
Fuzzy Rule-Bases (FRBs)	p. 3
The ANN-FRB Synergy	p. 4
Knowledge-Based Neurocomputing	p. 5
Knowledge Extraction from ANNs	p. 5
Knowledge-Based Design of ANNs	p. 9
The FARB: A Neuro-fuzzy Equivalence	p. 11
The FARB	p. 13
Definition	p. 15
Input-Output Mapping	p. 18
The FARB-ANN Equivalence	p. 21
The FARB and Feedforward ANNs	p. 21
Example 1: Knowledge Extraction from a Feedforward ANN	p. 22
Example 2: Knowledge-Based Design of a Feedforward ANN	p. 24
The FARB and First-Order RNNs	p. 26
First Approach	p. 26
Example 3: Knowledge Extraction from a Simple RNN	p. 27
Second Approach	p. 28
Third Approach	p. 29
Example 4: Knowledge Extraction from an RNN	p. 30
Example 5: Knowledge-Based Design of an RNN	p. 32
The FARB and Second-Order RNNs	p. 33
Summary	p. 35
Rule Simplification	p. 37
Sensitivity Analysis	p. 37
A Procedure for Simplifying a FARB	p. 39
Knowledge Extraction Using the FARB	p. 41
The Iris Classification Problem	p. 41
The LED Display Recognition Problem	p. 44
Knowledge Extraction Using the FARB	p. 46
FARB Simplification	p. 46
Analysis of the FRB	p. 48
The L₄ Language Recognition Problem	p. 50
Formal Languages	p. 50
Formal Languages and RNNs	p. 51
The Trained RNN	p. 51
Knowledge Extraction Using the FARB	p. 53
Knowledge-Based Design of ANNs	p. 59
The Direct Approach	p. 60
KBD of an ANN Recognizing L₄	p. 60
The Modular Approach	p. 63
The Counter Module	p. 63
The Sequence-Counter Module	p. 66
The String-Comparator Module	p. 66
The String-to-Num Converter Module	p. 67
The Num-to-String Converter Module	p. 68
The Soft Threshold Module	p. 68
KBD of an RNN for Recognizing the Extended L₄ Language	p. 69
KBD of an RNN for Recognizing the AB Language	p. 71
KBD of an RNN for Recognizing the Balanced Parentheses Language	p. 72
KBD of an RNN for Recognizing the 0ⁿ 1ⁿ Language	p. 74
Conclusions and Future Research	p. 77
Future Research	p. 77
Regularization of Network Training	p. 78
Extracting Knowledge during the Learning Process	p. 79
Knowledge Extraction from Support Vector Machines	p. 79
Knowledge Extraction from Trained Networks	p. 80
Proofs	p. 83
Details of the LED Recognition Network	p. 87
References	p. 89
Index	p. 99
Table of Contents provided by Ingram. All Rights Reserved.

Knowledge-Based Neurocomputing

A Fuzzy Logic Approach

At a Glance

Hardcover

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How to return your order

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