A new approach to fuzzy classifier systems and its application in self-generating neuro-fuzzy systems

A classifier system is a machine learning system that learns syntactically simple string rules (called classifiers) through a genetic algorithm to guide its performance in an arbitrary environment. In a classifier system, the bucket brigade algorithm is used to solve the problem of credit assignment, which is a critical problem in the field of reinforcement learning. In this paper, we propose a new approach to fuzzy classifier systems and a neuro-fuzzy system referred to as ACSNFIS to implement the proposed fuzzy classifier system. The proposed system is tested by the balancing problem of a cart pole and the back-driving problem of a truck to demonstrate its performance. r 2005 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2006
• Where: IJON
• Authors: Mu-Chun Su, Chien-Hsing Chou, Eugene Lai, Jonathan Lee
• Keywords: Classifier Systems, Fuzzy Classifier, Genetic Algorithm, IJON 2006