Identification of Hammerstein nonlinear ARMAX systems |
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| Authors: | Feng Ding [Author Vitae] Tongwen Chen [Author Vitae] |
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| Affiliation: | a Control Science and Engineering Research Center, Southern Yangtze University, Wuxi 214122, China
b Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2V4 |
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| Abstract: | Two identification algorithms, an iterative least-squares and a recursive least-squares, are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models. The basic idea is to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates. Convergence properties of the recursive algorithm in the stochastic framework show that the parameter estimation error consistently converges to zero under the generalized persistent excitation condition. The simulation results validate the algorithms proposed. |
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| Keywords: | Recursive identification Parameter estimation Convergence properties Stochastic gradient Least squares Hammerstein models Wiener models Martingale convergence theorem |
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