A Self-Stabilizing Neural Algorithm for Total Least Squares Filtering

被引：4

作者：

Kong Xiang-yu ^{[1
]}

Hu Chang-hua ^{[1
]}

Han Chong-zhao ^{[2
]}

机构：

[1] Res Inst High Technol, Xian 710025, Shaanxi, Peoples R China

[2] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2009年 / 30卷 / 03期

关键词：

Total least square filtering; FIR filters; Linear system identification; Adaptive algorithm; DISCRETE-TIME DYNAMICS; COMPONENT ANALYSIS; LEARNING ALGORITHMS; MINOR COMPONENTS; PRINCIPAL; CONVERGENCE; SUBSPACE;

D O I：

10.1007/s11063-009-9122-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A neural approach for solving the total least square (TLS) problem is presented in the paper. It is based on a linear neuron with a self-stabilizing neural algorithm, capable of resolving the TLS problem present in the parameter estimation of an adaptive FIR filters for system identification, where noisy errors affect not only the observation vector but also the data matrix. The learning rule is analyzed mathematically and the condition to guarantee the stability of algorithm is educed. The computer simulations are given to illustrate that the neural approach is self-stabilizing and considerably outperforms the existing TLS methods when a larger learning factor is used or the signal-noise-rate is lower.

引用

页码：257 / 271

页数：15

共 43 条

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