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This paper presents an auxiliary model based stochastic gradient parameter estimation algorithm for multi-input output-error systems by minimizing a quadratic cost function. The basic idea is to replace the unknown variables in the information vector with the outputs of an auxiliary model or estimated outputs and the analysis and simulation results indicate(More)
This paper studies the convergence of the auxiliary model based stochastic gradient parameter estimation algorithm for multi-input output-error systems by using the martingale convergence theorem. The basic idea is to formulate a positive definite function of the parameter estimation error and to indicate that the parameter estimates converge to their true(More)
This paper uses an estimated noise transfer function to filter the input-output data and presents a filtering based recursive least squares algorithm for ARMAX models. Through the data filtering, we obtain two identification models, one including the parameters of the system model, and the other including the parameters of the noise model. Thus, the(More)
— This paper uses the polynomial transformation technique to transform an ARX model into a special model that can be identified with dual-rate input-output data, and presents the residual based stochastic gradient algorithm for dual-rate sampled-data systems, and studies convergence properties of the algorithm involved. The analysis indicates that the(More)
A hierarchical parameter estimation algorithm is proposed to estimate the parameters of single-input multi-output (SIMO) system based on the auxiliary model. The key is to combine the auxiliary model identification idea and the hierarchical identification principle: decompose a system into two subsystems and replace the unknown variables in the information(More)
Since the multi-innovation stochastic gradient (MISG) method can produce highly accurate parameter estimates for linear regression models, this paper extends the MISG method to nonlinear regression models and presents an MISG algorithm for Hammer stein nonlinear CAR (ARX) systems with memory less nonlinear blocks followed by controlled autoregressive(More)
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