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This paper studies the problem of the parameter estimation for a class of closed-loop nonlinear systems. Based on the closed-loop identification scheme, a filtering based multi-innovation stochastic gradient algorithm is presented for estimating parameters of closed-loop Hammerstein nonlinear systems. In order to reduce the computation complexity, a hierarchical estimation approach is proposed by...
In this paper, we extends the innovation vector to the innovation matrices and presents a filtering based multi-innovation extended stochastic gradient algorithm for multi-input multi-output controlled autoregressive moving average systems. The basic idea is using the filtering technique to transform a multivariable system into two identification models, then to identify the parameters of these two...
In order to improve the parameter estimation accuracy for equation-error ARMA systems, a new identification method is derivedd in this paper. By means of the model equivalence theory, the proposed algorithm reduces the number of the unknown noise terms in the information vector of the identification model to acquire good precision. Simultaneously, the generalized extended least squares method is given...
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 parameter estimation error consistently converges...
A recursive least-squares identification algorithm is developed for Hammerstein nonlinear models, which consist of memoryless nonlinear blocks followed by linear dynamical systems described by controlled auto-regression (CAR) models. Convergence analysis of the proposed algorithms indicates that the parameter estimation error consistently converges to zero under proper conditions. The simulation results...
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