DESIGN OF DYNAMIC NONLINEAR INTELLIGENT CONTROL SYSTEM FOR SIGNAL PROCESSING
Zhiyou Wang,
Ying Chen,
Rayanatteah Alsemmeari () and
Yuan Zhou
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Zhiyou Wang: School of Electronic Communication and Electrical Engineering, Changsha University, Kaifu District, Changsha, P. R. China†Hunan Engineering Technology Research Center of Optoelectronic Health Detection, Changsha, P. R. China
Ying Chen: School of Electronic Communication and Electrical Engineering, Changsha University, Kaifu District, Changsha, P. R. China†Hunan Engineering Technology Research Center of Optoelectronic Health Detection, Changsha, P. R. China
Rayanatteah Alsemmeari: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Yuan Zhou: School of Electronic Communication and Electrical Engineering, Changsha University, Kaifu District, Changsha, P. R. China†Hunan Engineering Technology Research Center of Optoelectronic Health Detection, Changsha, P. R. China
FRACTALS (fractals), 2022, vol. 30, issue 02, 1-12
Abstract:
Based on back propagation neural network (BPNN), we design a dynamic nonlinear intelligent control system for signal processing and analysis in this work. Performances of the traditional PID and BPNN control systems is compared in a case of heat power engineering system design. In the design, the training times, regulation time of the BPNN control system are evaluated when k = −1.7 and k = −2.0, respectively. Furthermore, the regulation time, oscillation time and amplitude of the traditional PID and BPNN control systems are compared in the dead zone. Results of the comparison show that the BPNN based dynamic nonlinear intelligent control system demonstrates superior performances over the traditional PID control system. The research results prove the advantage of BPNN in the design of nonlinear control systems and provide a scientific and effective reference for the follow-up research works.
Keywords: Nonlinear System; Back Propagation Neural Network; Intelligent Control (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:30:y:2022:i:02:n:s0218348x22400916
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DOI: 10.1142/S0218348X22400916
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