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In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information (?)y/(?)u for optimizing the parameters of controller. Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of the proposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error, with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC), are conducted. In addition, the rates of convergence of
In this paper, an intelligent control system based on recurrent neural network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural network is used as a controller (RNFNC) to control a process adaptively and a recurrent neural network based on Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller systems. , recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of the proposed control system, the presented control system is applied to a continuously stirred tank reactor (CSTR). control effect and output error, with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC), are conducted. s of convergence of