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For input nonlinear output error moving average systems with a two-segment piecewise nonlinearity, a data filtering based stochastic gradient algorithm is developed to estimate the parameters of this nonlinear system based on the data filtering. The basic idea is to combine the key-term separation principle and the data filtering technique, and to decompose the identification model into two models...
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...
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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