Performance analysis of the auxiliary models based multi-innovation stochastic gradient estimation algorithm for output error systems

Authors:
Dongqing Wang;Feng Ding
Affiliations:
College of Automation Engineering, Qingdao University, Qingdao 266071, PR China and School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, PR China;School of Communication and Control Engineering, Jiangnan University, Wuxi 214122, PR China
Venue:
Digital Signal Processing
Year:
2010

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Abstract

This paper combines the multi-innovation identification theory and the auxiliary model identification idea and presents an auxiliary model based multi-innovation stochastic gradient algorithm by expanding the scalar innovation to an innovation vector and introducing the innovation length. Convergence analysis in the stochastic framework indicates that the parameter estimates given by the proposed algorithm can fast converge to their true values. Finally, we illustrate and test the proposed algorithm with an example.