Abstract
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.
Highlights
Building models of real systems is a central topic in many disciplines of engineering and science
A program written in C++ language was used to generate the neural networks model and Matlab was used to generate the fuzzy model
The results show how promising Artificial Intelligence (AI)-type modeling is
Summary
Building models of real systems is a central topic in many disciplines of engineering and science. A model of the plant can be used to design a feedback controller or to predict the future plant behavior in order to calculate optimal control actions [1]. Many of the recent developed computer control techniques are grouped into a research area called Intelligent Control, that result from the integration of Artificial Intelligent techniques within automatic control systems [2]. There is currently a significant and growing interest in the application of Artificial Intelligence (AI) type models to the problems involved with modeling the dynamics of complex, nonlinear processes. By far the most popular type of AI model for these purposes has been the neural networks, which attempts to produce „intelligent‟ behavior by recreating the hardware involved in the
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