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第三十届中国控制会议

作者： SONG Qingsong~(1,2),ZHAO Xiangmo~1,FENG Zuren~2 1.School of Information Engineering,Chang''an University,Xi''an 710064,P.R.China 2.Systems Engineering Institute,Xi''an Jiaotong University,Xi''an 710049,P.R.China

＜正＞In order to model nonlinear systems with more accuracy,and to further exploit the potential capacities of recurrent neural networks,we propose a novel recursive least square（RLS） algorithm based on echo state network（ESN）,and note it as RLSESN in this *** is a new paradigm for using recurrent neural networks（RNN） with a simpler training *** proposed RLSESN consists of three main components:an ESN,a recursive least square（RLS） algorithm with adaptive forgetting factor and a change detection *** first,the change detection module modifies the forgetting factor online according to ESN output *** then,the RLS algorithm regulates the ESN output connection *** simulation experiment results show that RLSESN can model nonlinear systems very well;the modeling performances are significantly better than those traditional ARMA model based filters.

关键词： Neural Networks System Identification recursive least squares algorithm Filtering

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Forward/backward prediction solution for adaptive noisy FIR filtering

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Science in China(Series F) 2009年第6期52卷 1007-1014页

作者： JIA LiJuan TAO Ran WANG Yue WADA Kiyoshi Department of Electronic Engineering Beijing Institute of Technology Beijing 100081 China Department of Electrical and Electronic System Engineering Kyushu University Fukuoka Japan

An important and hard problem in signal processing is the estimation of parameters in the presence of observation *** this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is considered and two developed bias compensation least squares （BCLS） methods are *** introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed *** exploiting the statistical properties of the cross-correlation function between the least squares （LS） error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be *** results are presented to illustrate the good performances of the proposed algorithms.

关键词： adaptive FIR filtering recursive least squares algorithm bias compensation forward prediction backward prediction

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维普期刊数据库

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Blind Equalization under Noisy Environment using Bias-compensated RLS method

Blind Equalization under Noisy Environment using Bias-compen...

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第40届中国控制会议

作者： Zhen Zhang Lijuan Jia Shunshoku Kanae Zi-Jiang Yang School of Information and Electronics Beijing Institute of Technology Department of Medical Engineering Faculty of Health SciencesJunshin Gakuen University Department of Intelligent Systems Engineering Ibaraki University

In this paper,a new blind adaptive equalization algorithm under noisy environment is *** consider a practical case where the noise of each transmission channel is *** oversampling the channel output at twice the symbol rate,a single-input double-output channel can be *** apply the recursive-least-squares（RLS） to tackle the blind equalization *** the noise-induced bias,RLS algorithm is *** order to eliminate the bias,we present a bias-compensated RLS（BCRLS） algorithm that can estimate the unknown additive noise online and the noise-induced bias can be therefore *** unbiased estimate of the channel characteristics obtained can be used for channel *** results are presented to demonstrate the performance of the proposed algorithm.

关键词： Blind Equalization recursive least squares algorithm Variance Estimation Bias-compensated Method

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