Rule generation for hierarchical fuzzy systems

A new method of rule generation for hierarchical fuzzy systems, called a hierarchical fuzzy associative memory (HIFAM) is described. A HIFAM is structured as a binary tree and overcomes the exponential growth of the rule bases when the number of inputs increases. The training algorithm for the HIFAM...

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Bibliographic Details
Published in1997 Annual Meeting of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.97TH8297) pp. 444 - 449
Main Author Holve, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Approximation algorithms
Artificial neural networks
Associative memory
Binary trees
Classification tree analysis
Costs
Decision trees
Fuzzy systems
Knowledge based systems
Training data
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Summary:A new method of rule generation for hierarchical fuzzy systems, called a hierarchical fuzzy associative memory (HIFAM) is described. A HIFAM is structured as a binary tree and overcomes the exponential growth of the rule bases when the number of inputs increases. The training algorithm for the HIFAM is suitable for approximation and classification problems. Several benchmarks demonstrate that the proposed method compares well with existing learning techniques like artificial neural networks and decision trees.
ISBN:0780340787
9780780340787
DOI:10.1109/NAFIPS.1997.624082