Multi-innovation Extended Stochastic Gradient Algorithm and Its Performance Analysis

被引：0
作者：
Yanjun Liu
Li Yu
Feng Ding
机构：
[1] Jiangnan University,School of Communication and Control Engineering
来源：
Circuits, Systems and Signal Processing | 2010年 / 29卷
关键词：
Recursive identification; Parameter estimation; Signal processing; Multi-innovation identification; Stochastic gradient; Performance analysis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：649 / 667
页数：18
相关论文
共 39 条
[1]  
Ding F.(2005)Parameter estimation of dual-rate stochastic systems by using an output error method IEEE Trans. Autom. Control 50 1436-1441
[2]  
Chen T.(2007)Performance analysis of multi-innovation gradient type identification methods Automatica 43 1-14
[3]  
Ding F.(2003)Multi-innovation stochastic gradient identification method Control Theory Appl. 20 870-874
[4]  
Chen T.(2007)Multi-innovation least squares identification methods based on the auxiliary model for MISO systems Appl. Math. Comput. 187 658-668
[5]  
Ding F.(2008)Amendments to “Performance analysis of estimation algorithms of non-stationary ARMA processes” IEEE Trans. Signal Process. 56 4983-4984
[6]  
Xiao D.Y.(2008)Performance analysis of stochastic gradient algorithms under weak conditions Sci. China Ser. F Inf. Sci. 51 1269-1280
[7]  
Ding T.(2009)Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises Signal Process. 89 1883–1890-554
[8]  
Ding F.(2009)Multi-innovation stochastic gradient algorithms for multi-input multi-output systems Digit. Signal Process. 19 545-1449
[9]  
Chen H.B.(2009)Identification for multirate multi-input systems using the multi-innovation identification theory Comput. Math. Appl. 57 1438-1003
[10]  
Li M.(2008)Auxiliary models based multi-innovation generalized extended stochastic gradient algorithms Control Decis. 23 999-762
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