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

Improved RLS algorithm for nonlinear time-varying system identification based on feed forward neural networks

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,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"。

An improved recursive least square (IRLS) method was proposed and applied in nonlinear time-varying system identification together with the feed forward neural network. Theoretic analysis and two simulation examples were 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.