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Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling. (English) Zbl 1349.93391

Summary: This paper decomposes a Hammerstein nonlinear system into two subsystems, one containing the parameters of the linear dynamical block and the other containing the parameters of the nonlinear static block, and presents a hierarchical multi-innovation stochastic gradient identification algorithm for Hammerstein systems based on the hierarchical identification principle. The proposed algorithm is simple in principle and easy to implement on-line. A simulation example is provided to test the effectiveness of the proposed algorithm.

MSC:

93E12 Identification in stochastic control theory
62F10 Point estimation
62J05 Linear regression; mixed models
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