Knowledge-Based Neurocomputing: A Fuzzy Logic Approach (Paperback)

Eyal Kolman

ISBN 10: 3642099858 / ISBN 13: 9783642099854

Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010

About this Item

Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783642099854

Bibliographic Details

• Title: Knowledge-Based Neurocomputing: A Fuzzy Logic Approach (Paperback)
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin
• Publication Date: 2010
• Language: English
• ISBN 10: 3642099858
• ISBN 13: 9783642099854
• Binding: Paperback
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

This book details the state-of-the-art in knowledge-based neurocomputing. It introduces a novel fuzzy-rule base known as Fuzzy All-permutations Rule-Base (FARB) and presents new connections between artificial neural networks and FARB.

From the Back Cover

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.