Identification and Prediction of Nonlinear Dynamical Plants Using TSK and Wavelet Neuro-Fuzzy Models

Author

Banakar, Ahmad ; Azeem, Mohammad Fazle

Author_Institution

Dept. of Electr. Eng., Aligarh Muslim Univ.

fYear

2006

fDate

Sept. 2006

Firstpage

617

Lastpage

620

Abstract

The problem of identification consists of setting up a suitably parameterized identification model and adjusting the parameters of the model by optimizing a performance index. Parallel and parallel-series identification methods are used to adjust an unknown model´s parameters. In this paper a combined parallel/series-parallel identification model, based on TSK fuzzy model and wavelet neuro-fuzzy model, is proposed

Keywords

fuzzy neural nets; identification; nonlinear dynamical systems; performance index; wavelet transforms; TSK fuzzy model; nonlinear dynamical plant identification; nonlinear dynamical plant prediction; parallel-series identification; parameterized identification model; performance index optimization; unknown model parameter; wavelet neurofuzzy model; Feedforward neural networks; Fuzzy systems; Heuristic algorithms; Intelligent systems; Mathematical model; Neural networks; Power system modeling; Predictive models; Time frequency analysis; Uncertainty; Parallel and Series-Parallel identification model; TSK fuzzy model; Wavelet Neuro-Fuzzy model;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2006 3rd International IEEE Conference on

Conference_Location

London

Print_ISBN

1-4244-01996-8

Electronic_ISBN

1-4244-01996-8

Type

conf

DOI

10.1109/IS.2006.348490

Filename

4155497