Paper Info
Title
Fuzzy Classifier System Architectures For Mobile Robotics: An Experimental Comparison
Abstract
We present an experimental comparison between two approaches to optimization of the rules for a fuzzy controller. More specifically, the problem is autonomous acquisition of an "investigative" obstacle avoidance competency for a mobile robot. We report on results from investigating two alternative approaches to the use of a Learning Classifier System (LCS) to optimize the fuzzy rule base. One approach operates at the level of whole rule bases, the "Pittsburgh" LCS. The other approach operates at the level of individual rules, the "Michigan" LCS. In this work, both of these Fuzzy Classifier Systems were designed to operate only on the rules of fuzzy controllers, with predefined fuzzy membership functions. There are two main results from this work. First, both approaches were capable of producing fuzzy controllers with subtle interactions between rules leading to competencies exceeding that of the hand-coded fuzzy controller presented in this article. Second, the Michigan approach suffered more seriously than the Pittsburgh approach from the well-known LCS "cooperation/competition" problem, which is accentuated here by the structural combination of Evolutionary Computation and a fuzzy system. This problem was alleviated a little by the combination of a clustered subpopulation niche system and a fitness-sharing scheme applied to the Michigan approach, but still remains. (C) 2007 Wiley Periodicals, Inc.
Year
DOI
Venue
2007
10.1002/int.20235
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Keywords
Field
DocType
mobile robot,system architecture
Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Fuzzy control system,Fuzzy associative matrix,Machine learning,Mathematics,Fuzzy rule
Journal
Volume
Issue
ISSN
22
9
0884-8173
Citations 
PageRank 
References 
2
0.41
13
Authors
2
Name
Order
Citations
PageRank
Anthony G. Pipe125539.08
Brian Carse225926.31