Decomposition-Based Recursive Least Squares Algorithm for Wiener Nonlinear Feedback FIR-MA Systems Using the Filtering Theory

被引：0
作者：
Ziyun Wang
Yan Wang
Zhicheng Ji
机构：
[1] Jiangnan University,Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)
来源：
Circuits, Systems, and Signal Processing | 2014年 / 33卷
关键词：
System identification; Wiener model; Recursive least squares algorithms ; Filtering theory; Key-term decomposition;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A decomposition-based recursive least squares algorithm is developed for Wiener nonlinear systems described by finite impulse response moving average models. After transferring a finite impulse response moving average (FIR-MA) model to a controlled autoregressive model, we compute the parameters by combining the decomposition principle and the least squares method and using the filtering idea. The simulation results validate the proposed algorithm.
引用
收藏
页码：3649 / 3662
页数：13
相关论文
共 81 条
[1]  
Ahmad MS(2011)Recursive inverse adaptive filtering algorithm Digit. Signal Process. 21 491-496
[2]  
Kukrer O(2008)Recursive parameter identification for infinite-dimensional factor model by using particle filter-application to US-Treasury Bonds Int. J. Innovative Comput. Inf. Control 4 35-52
[3]  
Hocanin A(2011)SSHI methods for solving general linear matrix equations Eng. Comput. 28 1028-1043
[4]  
Aihara SI(2012)Iterative algorithms for the generalized centro-symmetric and central anti-symmetric solutions of general coupled matrix equations Eng. Comput. 29 528-560
[5]  
Bagchi A(2012)Fourth-order variants of Newton’s method without second derivatives for solving non-linear equations Eng. Comput. 29 356-365
[6]  
Dehghan M(2005)Gradient based iterative algorithms for solving a class of matrix equations IEEE Trans. Autom. Control 50 1216-1221
[7]  
Hajarian M(2007)Performance analysis of multi-innovation gradient type identification methods Automatica 43 1-14
[8]  
Dehghan M(2009)Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises Signal Process. 89 1883-1890
[9]  
Hajarian M(2010)Multi-innovation least squares identification for linear and pseudo-linear regression models IEEE Trans. Syst. Man Cybern. Part B 40 767-778
[10]  
Dehghan M(2010)Several multi-innovation identification methods Digit. Signal Process. 20 1027-1039
123456789