Aitken based modified Kalman filtering stochastic gradient algorithm for dual-rate nonlinear models |
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| Institution: | 1. School of Science, Jiangnan University, Wuxi 214122, PR China;2. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China;3. Department of Engineering Design and Mathematics, University of the West of England, Bristol BS16 1QY, UK;1. Electrical and Computer Engineering Faculty, Hakim Sabzevari University, Sabzevar, Iran;2. Automatic Control and System Engineering Department, University of the Basque Country, UPV/EHU, Nieves Cano 12, Vitoria, Spain;1. Institute of Intelligence Science and Engineering, Shenzhen Polytechnic, Shenzhen 518055, China;2. College of Automation, Harbin Engineering University, Harbin, China;3. National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Yuquan Campus, Hangzhou, Zhejiang 310027, China |
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| Abstract: | This paper develops an Aitken based modified Kalman filtering stochastic gradient algorithm for dual-rate nonlinear models. The Aitken based method can increase the convergence rate and the modified Kalman filter can improve the estimation accuracy. Thus compared to the traditional auxiliary model based stochastic gradient algorithm, the proposed algorithm in this paper is more effective, and this is proved by the convergence analysis. Furthermore, two simulated examples are given to illustrate the effectiveness of the proposed algorithm. |
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