Markov Chain Approach to Identification of Wiener, Hammerstein and NARX Systems
نویسندگان
چکیده
A Markov chain approach to identification of the Wiener, Hammerstein, and nonlinear ARX (NARX) systems is presented. The motivation of this approach comes from the fact that these classes of nonlinear systems are connected with Markov chains, and hence their asymptotical properties, such as ergodicity, stationarity, and invariant probability distribution, can be derived from the corresponding chains. The estimates are given by the stochastic approximation algorithms with expanding truncations (SAAWET). All estimates proposed in the paper are strongly consistent. Numerical examples support the validity of this approach.
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