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Math. Comput. Appl. 2003, 8(2), 245-252; doi:10.3390/mca8020245

Fuzzy Inference System Based on Neural Network for Technological Process Control

Near East University, Department of Computer Engineering, P.O. Box 670, Letkosha, TRNC, Mersin-lO, Turkey
Published: 1 August 2003
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Abstract

The implementation of a fuzzy system for technological process control based on parallel architecture and learning capabilities of neural networks is considered. The algorithms of fuzzy inference system on neural network (neuro-fuzzy system) are described. To train unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are generated. The neuro-fuzzy system is applied to control a dynamic plant. Using desired time response characteristics of the system the synthesis of neuro-fuzzy controller for technological process control is carried out. The simulation result of the neuro-fuzzy control system is compared with the simulation results of control systems based on PID- and neural controller. It is found that the neuro-fuzzy control system has better control performance than the others.
Keywords: uzzy logic; neural network; neuro-fuzzy system; control system uzzy logic; neural network; neuro-fuzzy system; control system
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Abiyev, R. Fuzzy Inference System Based on Neural Network for Technological Process Control. Math. Comput. Appl. 2003, 8, 245-252.

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