A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System | Zendy

Metin Demirtaş | Zendy; Musa Alcı | Zendy

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A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

Author(s) -

Metin Demirtaş,

Musa Alcı

Publication year - 2011

Publication title -

an international journal of optimization and control theories and applications (ijocta)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.287

H-Index - 6

eISSN - 2146-5703

pISSN - 2146-0957

DOI - 10.11121/ijocta.01.2011.0055

Subject(s) - artificial neural network , nonlinear system , computer science , neuro fuzzy , sensitivity (control systems) , fuzzy logic , set (abstract data type) , system dynamics , control engineering , control theory (sociology) , artificial intelligence , machine learning , fuzzy control system , engineering , control (management) , physics , quantum mechanics , electronic engineering , programming language

The aim of this paper is to compare the neural network and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC) motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

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