A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications
In this paper a new artificial neural network based fuzzy inference system (ANNBFIS) has been described. The novelty of the system consists in the moving fuzzy consequent in if-then rules. The location of this fuzzy set is determined by a linear combination of system inputs. This system also automatically generates rules from numerical data. The proposed system operates with Gaussian membership functions in premise part. Parameter estimation has been made by connection of both gradient and least-squares methods. For initialization of unknown parameter values of premises, a preliminary fuzzy c-means clustering method has been employed. For evaluation of the number of if-then rules, the indexes of Xie-Beni and Fukujama-Sugeno have been applied. The applications to prediction of chaotic time series, pattern recognition and system identification are considered in this paper as well. (C) 1999 Published by Elsevier Science B.V. All rights reserved.
neural network prediction pattern recognition system identification fuzzy system automatic rule generation