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2013 Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition
Huiyi Hu, Xiao Yongsong, Rui Ding
J. Appl. Math. 2013: 1-7 (2013). DOI: 10.1155/2013/596141

Abstract

An input nonlinear system is decomposed into two subsystems, one including the parameters of the system model and the other including the parameters of the noise model, and a multi-innovation stochastic gradient algorithm is presented for Hammerstein controlled autoregressive autoregressive (H-CARAR) systems based on the key term separation principle and on the model decomposition, in order to improve the convergence speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.

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Huiyi Hu. Xiao Yongsong. Rui Ding. "Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition." J. Appl. Math. 2013 1 - 7, 2013. https://doi.org/10.1155/2013/596141

Information

Published: 2013
First available in Project Euclid: 14 March 2014

zbMATH: 06950772
MathSciNet: MR3115280
Digital Object Identifier: 10.1155/2013/596141

Rights: Copyright © 2013 Hindawi

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