Training of fuzzy logic systems using nearest neighborhood clustering

Author

Wang, Li-Xin

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA

fYear

1993

fDate

1993

Firstpage

13

Abstract

The author first constructs an optimal fuzzy logic system which is capable of matching all the input-output pairs in the training set to arbitrary accuracy. Then an adaptive version of the optimal fuzzy logic system is presented, using the nearest neighborhood clustering algorithm. To do this, clusters of the sample data using the nearest neighborhood clustering algorithm are viewed as sample data and the optimal fuzzy logic system is used as an adaptive controller for nonlinear dynamic systems. The simulation results showed that the adaptive fuzzy controller could produce very good tracking control

Keywords

adaptive control; fuzzy logic; learning (artificial intelligence); nonlinear control systems; optimal systems; pattern recognition; adaptive controller; input-output pairs matching; nearest neighborhood clustering; nonlinear dynamic systems; optimal fuzzy logic system; tracking control; Adaptive control; Clustering algorithms; Control systems; Fuzzy control; Fuzzy logic; Impedance matching; Nonlinear control systems; Nonlinear dynamical systems; Optimal control; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1993., Second IEEE International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-0614-7

Type

conf

DOI

10.1109/FUZZY.1993.327471

Filename

327471