The fuzzy classifier system: Motivations and first results

Parallel Problem Solving from Nature - Lecture Notes in Computer Science ◽

10.1007/bfb0029774 ◽

1991 ◽

pp. 338-342 ◽

Author(s):

Manuel Valenzuela-Rendón

Keyword(s):

Fuzzy Classifier ◽

Classifier System ◽

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First Results from Experiments in Fuzzy Classifier System Architectures for Mobile Robotics

Parallel Problem Solving from Nature — PPSN VII - Lecture Notes in Computer Science ◽

10.1007/3-540-45712-7_56 ◽

2002 ◽

pp. 578-587 ◽

Author(s):

A. G. Pipe ◽

B. Carse

Keyword(s):

Mobile Robotics ◽

Fuzzy Classifier ◽

System Architectures ◽

Classifier System ◽

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The Compact Classifier System: Scalability Analysis and First Results

2005 IEEE Congress on Evolutionary Computation ◽

10.1109/cec.2005.1554737 ◽

2005 ◽

Author(s):

X. Llora ◽

K. Sastry ◽

D.E. Goldberg

Keyword(s):

Classifier System ◽

Scalability Analysis ◽

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Distributed Routing in Communication Networks using the Temporal Fuzzy Classifier System — a Study on Evolutionary Multi-Agent Control

Applications of Learning Classifier Systems - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-540-39925-4_9 ◽

2004 ◽

pp. 203-222

Author(s):

Brian Carse ◽

Terence C. Fogarty ◽

Alistair Munro

Keyword(s):

Communication Networks ◽

Fuzzy Classifier ◽

Distributed Routing ◽

Classifier System ◽

System A ◽

Multi Agent ◽

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Fuzzy classifier system architectures for mobile robotics: An experimental comparison

International Journal of Intelligent Systems ◽

10.1002/int.20235 ◽

2007 ◽

Vol 22 (9) ◽

pp. 993-1019 ◽

Author(s):

A.G. Pipe ◽

B. Carse

Keyword(s):

Mobile Robotics ◽

Experimental Comparison ◽

Fuzzy Classifier ◽

System Architectures ◽

Classifier System

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Heterogeneous face matching using geometric edge-texture feature ( GETF ) and multiple fuzzy-classifier system

Applied Soft Computing ◽

10.1016/j.asoc.2015.12.006 ◽

2016 ◽

Vol 46 ◽

pp. 967-979 ◽

Author(s):

Hiranmoy Roy ◽

Debotosh Bhattacharjee

Keyword(s):

Texture Feature ◽

Fuzzy Classifier ◽

Classifier System ◽

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A study on apportionment of credits of fuzzy classifier system for knowledge acquisition of large scale systems

Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference ◽

10.1109/fuzzy.1994.343587 ◽

2002 ◽

Author(s):

K. Nakaoka ◽

T. Furuhashi ◽

Y. Uchikawa

Keyword(s):

Knowledge Acquisition ◽

Large Scale ◽

Fuzzy Classifier ◽

Large Scale Systems ◽

Classifier System

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Design of optimal fuzzy classifier system using Particle Swarm Optimization

2010 International Conference on Innovative Computing Technologies (ICICT) ◽

10.1109/icinnovct.2010.5440088 ◽

2010 ◽

Author(s):

C. Rani ◽

S.N. Deepa

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Fuzzy Classifier ◽

Swarm Optimization ◽

Classifier System

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A Fuzzy Classifier System for evolutionary learning of robot behaviors

Applied Mathematics and Computation ◽

10.1016/s0096-3003(97)10006-6 ◽

1998 ◽

Vol 91 (1) ◽

pp. 73-81 ◽

Author(s):

Yasushi Iwakoshi ◽

Takeshi Furuhashi ◽

Yoshiki Uchikawa

Keyword(s):

Evolutionary Learning ◽

Fuzzy Classifier ◽

Classifier System

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Explorations in Fuzzy Classifier System Architectures

Adaptive Computing in Design and Manufacture V ◽

10.1007/978-0-85729-345-9_19 ◽

2002 ◽

pp. 219-230

Author(s):

A. G. Pipe ◽

B. Carse

Keyword(s):

Fuzzy Classifier ◽

System Architectures ◽

Classifier System

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A Rule Discovery by Fuzzy Classifier System Utilizing Symbolic Information

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2000.p0024 ◽

2000 ◽

Vol 4 (1) ◽

pp. 24-30

Author(s):

Makoto Fujii ◽

◽

Takeshi Furuhashi

Keyword(s):

Search Space ◽

Fuzzy Rules ◽

Rule Discovery ◽

Human Knowledge ◽

Fuzzy Classifier ◽

Classifier System ◽

Symbolic Information ◽

Efficient Exploration

This paper presents a new fuzzy classifier system (FCS) that can discover effective fuzzy rules efficiently. The system incorporates human knowledge in the form of symbolic information, and effectively limits its search space for fuzzy rules by using knowledge. The system also extracts symbolic information from acquired fuzzy rules for efficient exploration of other new fuzzy rules. Simulations are done to demonstrate the feasibility of the proposed method.

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