- In this paper the implementation of a fuzzy system for technological process control using parallel architecture and teaming capabilities of neural networks is considered. The structure and algorithms of neuro-fuzzy inference system are described. To train unknown coefficients of the system, the supervised teaming algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. 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 neuro-fuzzy control system is compared with the simulation results of control systems based on PID- and neural controller. It is found that neuro-fuzzy control system has better control performance than the others.
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