用于混沌时间序列自适应预测的一种少参数二阶Volterra滤波器

研究了二阶Volterra滤波器的一种乘积耦合近似实现结构及其非线性NLMS自适应算法,并用这种少参数二阶Volterra滤波器(RPSOVF)研究了一些混沌信号的非线性自适应预测性能.仿真研究结果表明:所给出的非线性NLMS自适应算法能够保证这种RPSOVF的稳定性和收敛性,且RPSOVF用这种非线性NLMS自适应算法能够自适应预测一些混沌时间序列. 关键词： 混沌 非线性自适应预测 Volterra滤波器 非线性NLMS自适应算法

A REDUCED PARAMETER SECOND-ORDER VOLTERRA FILTER WITH APPLICATION TO NONLINEAR ADAPTIVE PREDICTION OF CHAOTIC TIME SERIES

A reduced parameter second-order Volterra filter (RPSOVF) which is constructed by the multiplication-coupled two linear FIR filters, and its nonlinear normalized least mean square (NLMS) algorithm is proposed; and this RPSOVF with nonlinear NLMS algorithm are used to make adaptive predictions of chaotic time series. The rule of selecting convergent assistant parameters of the nonlinear NLMS algorithm is obtained. Experimental results show that this reduced parameter second-order Volterra filter with the nonlinear NLMS algorithm can be successfully used to make adaptive predictions of chaotic time series, and the modified nonlinear NLMS algorithm enables RPSOVF to converge and stabilize.