Circuits, Systems and Signal Processing

, Volume 29, Issue 4, pp 649–667 | Cite as

Multi-innovation Extended Stochastic Gradient Algorithm and Its Performance Analysis

  • Yanjun Liu
  • Li Yu
  • Feng Ding


This paper derives the multi-innovation extended stochastic gradient algorithm for controlled autoregressive moving average models by expanding the scalar innovation to an innovation vector and analyzes its performance in detail. Four convergence theorems are given for the multi-innovation extended stochastic gradient algorithm to show that the parameter estimates converge to their true values under the weak persistent excitation condition. The simulation results show that the proposed algorithm can produce more accurate parameter estimates than the traditional extended stochastic gradient algorithm.


Recursive identification Parameter estimation Signal processing Multi-innovation identification Stochastic gradient Performance analysis 


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Communication and Control EngineeringJiangnan UniversityWuxiP.R. China

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