Title :
Rule generation for hierarchical fuzzy systems
Author_Institution :
Bavarian Res. Center for Knowledge-Based Syst., Erlangen, Germany
Abstract :
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
Keywords :
content-addressable storage; fuzzy logic; fuzzy systems; hierarchical systems; knowledge based systems; learning (artificial intelligence); pattern classification; tree data structures; uncertainty handling; HIFAM; approximation problems; artificial neural networks; benchmarks; binary tree structure; classification problems; decision trees; hierarchical fuzzy associative memory; hierarchical fuzzy systems; input number; learning techniques; rule base exponential growth; rule generation method; training algorithm; Approximation algorithms; Artificial neural networks; Associative memory; Binary trees; Classification tree analysis; Costs; Decision trees; Fuzzy systems; Knowledge based systems; Training data;
Conference_Titel :
Fuzzy Information Processing Society, 1997. NAFIPS '97., 1997 Annual Meeting of the North American
Conference_Location :
Syracuse, NY
Print_ISBN :
0-7803-4078-7
DOI :
10.1109/NAFIPS.1997.624082
