基于Levenberg-Marquardt算法和最小二乘方法的小波网络混合学习算法

本文针对小波网络现有学习算法的不足,把Levenberg-Marquardt算法(简称LM算法)和最小二乘算法有机地结合在一起,提出了一种新的小波网络混合学习算法.在该混合算法中LM算法用来训练小波网络的非线性参数,而最小二乘算法用来训练线性参数.最后以辩识一个混沌系统为例进行了数值仿真,并与改进的BP算法和单纯LM算法进行了比较,结果说明了所提算法具有很好的收敛性能和收敛速度.

A HYBRID LEARNING ALGORITHM BASED ON LEVENBERGMARQUARDT ALGORITHM AND LEAST SQUARES TECHNIQUES FOR WAVELET NETWORKS

A hybrid learning algorithm for wavelet networks is presented to quicken up the speed of convergence, which combines the Levenberg Marquardt (LM) algorithm with least squares methods. The LM algorithm trains a wavelet network over a reduced weight space that consists of nonlinear parameters of the wavelet networks. The remained weights that are linear parameters are calculated in accordance with least square methods. Identification of a chaotic system is applied to demonstration of the performance of the proposed hybrid learning algorithm. The results indicate that the proposed algorithm is very efficient and enables the learning process to significantly speed up.