Multi-innovation gradient estimation algorithms and convergence analysis for feedback nonlinear equation-error moving average systems |
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| Institution: | 1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China;1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, PR China;2. Nonlinear Analysis and Applied Mathematics(NAAM)-Research Group, Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia;3. Key Laboratory of Jiangxi Province for Numerical Simulation and Emulation Techniques, Gannan Normal University, Ganzhou 341000, China;4. Department of Mathematics, Quaid-i-Azam University 45320, Isamabad 44000, Pakistan;1. Dep. of Systems and Automation Engineering, University of Seville, Seville, 41092, Spain;2. Dep. of Engineering, Loyola University Andalusia, Dos Hermanas, Seville, 41704, Spain;1. College of Engineering University of Hail Po.Box 2440, Hail, Kingdom of Saudi Arabia;2. National School of Engineering of Sfax, University of Sfax, Lab-STA, LR11ES50, 3038, Sfax, Tunisia;3. Modeling, Information, and Systems Laboratory, University of Picardie Jules Verne, UFR of Sciences, 33 Rue St Leu Amiens 80000, France |
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| Abstract: | Considering the colored noises from the process environments, the parameter estimation problems for the feedback nonlinear equation-error systems interfered by moving average noises are addressed in this paper. Due to small computational burden, the gradient search principle is adopted to the feedback nonlinear systems and an overall extended stochastic gradient algorithm is derived for parameter estimation. Introducing the innovation length, the scalar innovation is expanded into the innovation vector and a multi-innovation extended stochastic gradient algorithm is further developed to reach the high estimation accuracy by utilizing more dynamical observed data. Furthermore, to assure the convergence of the proposed algorithms, their convergence properties are analyzed through the stochastic process theory. Finally, the experimental results indicate the effectiveness of the proposed algorithms. |
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