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For the input-output representation of non-uniformly multirate discrete-time systems, a coupled least squares algorithm is derived to estimate the model parameters with the advantage of avoiding the computation of matrix inversion. Moreover, The proposed algorithm has good convergence properties. The simulation test verifies the effectiveness of the algorithm.
A recursive generalized least squares and a generalized stochastic gradient algorithms are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks followed by linear dynamical blocks described by CARAR models (HCARAR models). The basic idea is to replace the unmeasurable noise terms in the information vectors with their estimates and to compute the noise estimates through different...
This paper derives a least squares based and a gradient based iterative identification algorithms for Wiener nonlinear systems. These methods separate one bilinear-parameter cost function into two linear-parameter cost functions, estimating directly the parameters of the Wiener systems. The simulation results confirm that the proposed two algorithms are valid and the least squares based iterative...
This paper develops a hierarchical extended stochastic gradient identification algorithms for MIMO ARMAX-like systems to deal with colored noises based on the hierarchical identification principle. The convergence performance of such algorithms is studied in detail; in particular, conditions for parameter estimation errors to converge to zero are established, which include persistent excitation of...
This paper derives an identification model for a class of stochastic systems with colored noises. The information vector in the identification model contains both unknown noise-free outputs (i.e., true outputs) and unmeasurable noise terms, this is difficulty of identification. This paper establishes an auxiliary model by using the measurable information of the system and replaces the unknown noise-free...
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