A polynomial fuzzy neural network for identification and control

Author

Kim, Sungshin ; Vachtsevanos, George J.

Author_Institution

Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

fYear

1996

fDate

19-22 Jun 1996

Firstpage

5

Lastpage

9

Abstract

This paper introduces a new neuro-fuzzy system, an effective optimization method through a genetic algorithm, a performance criterion for model selection, and a numerical example to illustrate the proposed modeling and control approach. The neuro-fuzzy system is based on the polynomial fuzzy neural network architecture. A new performance criterion is defined based on the Group Method of Data Handling; it minimizes the output error while preventing overfitting of the empirical data set. The neuro-fuzzy model is employed to provide optimum set points for low-level control activity

Keywords

data handling; fuzzy control; fuzzy neural nets; genetic algorithms; identification; modelling; neural net architecture; neurocontrollers; Group Method of Data Handling; data overfitting; genetic algorithm; identification; low-level control; model selection; neurofuzzy control; optimization method; output error minimization; performance criterion; polynomial fuzzy neural network; polynomial fuzzy neural network architecture; Computer networks; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Input variables; Least squares approximation; Neural networks; Optimization methods; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American

Conference_Location

Berkeley, CA

Print_ISBN

0-7803-3225-3

Type

conf

DOI

10.1109/NAFIPS.1996.534693

Filename

534693