Multivariable Hammerstein nonlinear systems contain a sum of some bilinear parameter functions, which is hard to convert into a standard regressive form for processing. The identification system can be converted into ...
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Multivariable Hammerstein nonlinear systems contain a sum of some bilinear parameter functions, which is hard to convert into a standard regressive form for processing. The identification system can be converted into two different regressive forms by using multiple sets of binary signals. By combining the multi-innovation theory with the weight matrix, a weighted multi-innovation extended stochastic gradient algorithm with a forgetting factor is presented to estimate the parameters of parallel nonlinear subsystems and a linear subsystem. The advantage of the proposed algorithm is that it achieves faster convergence rates and higher accurate estimates than hierarchical principle based extended stochastic gradient algorithm and over-parameterization based extended stochastic gradient algorithm. Examples of CSTR process and PV power generation system are provided respectively to demonstrate the feasibility of the identification algorithm. This indicates that the prediction accuracy of the proposed algorithm can be improved by weighting the innovation.
作者：
Xu, HuanDing, FengYang, ErfuJiangnan Univ
Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Qingdao Univ Sci & Technol
Coll Automat & Elect Engn Qingdao 266061 Peoples R China Univ Strathclyde
Strathclyde Space Inst Dept Design Mfg & Engn Management Space Mechatron Syst Technol Lab Glasgow G1 1XJ Lanark Scotland
This study focuses on the recursive parameter estimation problems for the non-linear exponential autoregressive model with moving average noise (the ExpARMA model for short). By means of the gradient search, an extend...
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This study focuses on the recursive parameter estimation problems for the non-linear exponential autoregressive model with moving average noise (the ExpARMA model for short). By means of the gradient search, an extendedstochasticgradient (ESG) algorithm is derived. Considering the difficulty of determining the step-size in the ESG algorithm, a numerical approach is proposed to obtain the optimal step-size. In order to improve the parameter estimation accuracy, the authors employ the multi-innovation identification theory to develop a multi-innovation ESG (MI-ESG) algorithm for the ExpARMA model. Introducing a forgetting factor into the MI-ESG algorithm, the parameter estimation accuracy can be further improved. With an appropriate innovation length and forgetting factor, the variant of the MI-ESG algorithm is effective to identify all the unknown parameters of the ExpARMA model. A simulation example is provided to test the proposed algorithms.
In this study, extended Kalman Filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (H-W) ARMAX models. The basic idea is to estimate the original parameters of the identification model...
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ISBN:
(纸本)9781424487363
In this study, extended Kalman Filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (H-W) ARMAX models. The basic idea is to estimate the original parameters of the identification model, which are appeared in the form of product terms, directly. While, other algorithms like extended Forgetting Factor stochasticgradient (EFG), extendedstochasticgradient (ESG), Forgetting Factor Recursive Least Square (FFRLS) and Kalman Filter (KF), estimate parameters in the product form and they need another algorithms such averaging method (AVE method), singular value decomposition method (SVD method) to separate the parameters. So, the computational complexity of the proposed approach decreases. To show the efficiency of this method the results are compared with EFG and ESG method.
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