基于前向神经网络的非线性时变系统辨识的改进递推最小二乘算法

于开平;牟晓明

振动与冲击 ›› 2009, Vol. 28 ›› Issue (6) : 107-109,.

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振动与冲击 ›› 2009, Vol. 28 ›› Issue (6) : 107-109,.
论文

基于前向神经网络的非线性时变系统辨识的改进递推最小二乘算法

  • 于开平, 牟晓明
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Improved RLS Algorithm for Nonlinear Time-Varying System Identification Based on Feed Forward Neural Networks

  • YU Kai-ping, MU Xiao-ming
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摘要

标准的递推最小二乘算法随着递推次数的增加,增益矩阵将逐渐趋于零,致使递推算法慢慢失去修正能力,出现所谓的“数据饱和”现象。为了克服“数据饱和”问题,本文首先对递推最小二乘算法进行改进,得到了改进的最小二乘算法(IRLS),并给出了收敛性证明,然后将该算法应用于基于前向神经网络的非线性时变系统辨识。通过对两个非线性时变系统进行有效验证,仿真结果表明本文算法计算精度高、计算速度快、数值稳定性好,并能有效克服“数据饱和”。

Abstract

Recursive least square (RLS) is an efficient approach to neural network training. However, for the classical RLS algorithm, during the iterations, its gain vector gradually decreases to zero and loses the ability of modification, which will lead to the so called “data saturation” phenomenon. This paper proposes an improved recursive least square (IRLS) and applies it to nonlinear time-varying system identification together with the feed forward neural network. Theoretic analysis and two simulation examples are given to demonstrate the effectiveness of the proposed IRLS. Simulation results show that the proposed IRLS can overcome the problem of “data saturation” and has higher accuracy and robustness.

关键词

非线性时变系统 / 多层前向神经网络 / 系统辨识 / 改进递推最小二乘算法

Key words

Nonlinear time-varying system / Multi-layer feed forward neural network / System identification / Improved RLS

引用本文

导出引用
于开平;牟晓明. 基于前向神经网络的非线性时变系统辨识的改进递推最小二乘算法[J]. 振动与冲击, 2009, 28(6): 107-109,
YU Kai-ping;MU Xiao-ming. Improved RLS Algorithm for Nonlinear Time-Varying System Identification Based on Feed Forward Neural Networks[J]. Journal of Vibration and Shock, 2009, 28(6): 107-109,
中图分类号: TP271    O322   

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