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Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle
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DOI：10.1016/j.amc.2014.09.070.png)
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In this paper, we use the hierarchical identification principle to decompose a Hammerstein controlled autoregressive system into three subsystems, apply the key term separation principle to express the system output as a linear combination of the system parameters, and then derive a hierarchical gradient parameter estimation algorithm for identifying all subsystems. Finally, a multi-innovation stochastic gradient algorithm is presented to improve the estimation accuracy by making full of the identification innovation. The simulation results show that the proposed algorithm is effective. (C) 2014 Elsevier Inc. All rights reserved.
Keyword:
Stochastic gradient
Parameter estimation
Hierarchical identification
Auxiliary model
Hammerstein system
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IF:
3.4
论文数:
2.3W
被引数:
3.3W
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