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- 저자 : Meihang Li (검색결과 2 건)
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Meihang Li,Ximei Liu 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.6
Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system modeling. This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of a class of bilinear systems. The input-output representation of a bilinear system is derived through eliminating the state variables in the model. Then, a filtering based maximum likelihood iterative least squares algorithm is proposed for identifying the parameters of bilinear systems with colored noises by filtering the input-output data with a filter. A least squares based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems. The filtering based maximum likelihood iterative least squares algorithm is more accurate under different noise variance, and has higher computational efficiency.
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Meihang Li,Ximei Liu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.7
The iterative parameter estimation methods for a class of nonlinear systems with interval-varying measurements are studied in this paper. According to the auxiliary model identification idea, an auxiliary model is constructed to estimate the unknown noise-free process outputs, and an interval-varying auxiliary model gradient-based iterative identification algorithm is developed. Furthermore, a particle filter, which uses some discrete random sampling points to approximate the posterior probability density function, is adopted to compute the output estimates. Then an interval-varying particle filtering gradient-based iterative algorithm is derived, and an interval-varying auxiliary model based stochastic gradient (V-AM-SG) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems with interval-varying measurements, and can generate more accurate parameter estimates than the V-AM-SG algorithm.
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