by University of Sheffield, Dept. of Control Engineering in Sheffield .
Written in English
|Statement||S. Chen and S.A. Billings.|
|Series||Research report / University of Sheffield. Department of Control Engineering -- no.322, Research report (University of Sheffield. Department ofControl Engineering) -- no.322.|
|Contributions||Billings, S. A.|
On-line identification of the parameters in a non-linear output-affine model is considered. The recursive maximum likelihood estimator, which was originally derived for linear systems, is extended. Sorry, our data provider has not provided any external links therefore we are unable to provide a link to the full by: 6. This paper combines polynomial chaos theory with maximum likelihood estimation for a novel approach to recursive parameter estimation in state-space systems. A simulation study compares the proposed approach with the extended Kalman filter to estimate the value of an unknown damping coefficient of a nonlinear Van der Pol by: Recursive Least Squares Identification Algorithms for Multiple-Input Nonlinear Box–Jenkins Systems Using the Maximum Likelihood Principle Parameters Identification for Nonlinear Dynamic Systems Via Genetic Algorithm Optimization,” Testing the Capital Asset Pricing Model With Local Maximum Likelihood Methods,” Math. Comput.
1. Introduction. System identification and parameter estimation play an important role in signal processing and adaptive control,,,,,,,.The maximum likelihood method has wide applications in system identification,.In the area of system identification, Zhang and Cui proposed a bias compensation recursive least squares method for stochastic systems with colored noise, and Zhang. This paper studies the identification problems of input nonlinear controlled autoregressive moving average (IN-CARMA) systems, and derived an auxiliary model based recursive extended least squares (AM-RELS) algorithm and a maximum likelihood algorithm based on the Newton optimization method. The simulation results show that the proposed algorithm are effective. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm, a particle filtering technique, which approximates the posterior probability density function with a weighted set of discrete. This paper studies the identification problem of multivariable controlled autoregressive moving average systems. For the case with a parameter matrix and an unmeasurable vector in the system identification model, we transform the model into several submodels based on the number of the outputs. A maximum likelihood-based recursive least-squares algorithm is derived to identify .
Section 2 formulates the problem and describes the identification model of the Wiener nonlinear systems. Section 3 derives a recursive least squares algorithm for the Wiener nonlinear model. Section 4 provides an illustrative example to verify the effectiveness of the proposed algorithm. Finally, some concluding remarks are given in Section 5. 2. In contrast to maximum likelihood estimation of model parameters in ordinary state-space modeling, for which the recursive filter computation has to be done many times, model parameter estimation. Ozkan E., Lindsten F., Fritsche C., Gustafsson ive maximum likelihood identification of jump Markov nonlinear systems IEEE Trans Signal Process, 63 . The identification of a non-linear output-affine difference equation model is considered. that the maximum likelihood recursive generalised least squares algorithm can effectively estimate the.