改进的非线性最小二乘算法训练多层前馈神经网络

Modified Nonlinear Least-Square Algorithm for Training Multilayer Feedforward Neural Networks

本文通过在普通非线性最小二乘算法的准则函数中加一个正则项，推导出一种改进的非线性最小二乘算法，包括算法的批处理形式和递推形式．使用该算法的递推形式训练多层前馈神经网络能克服病态、减少计算量和内存占用量．文中给出的仿真结果说明该算法具有比常用的ＢＰ算法更好的收敛性能．

A modified nonlinear least-square algorithm is derived from the approach appending a regularization term to the conventional nonlinear least-square criterion,ineluding the batch and recursive versions. Training multilayer feed forward neural networks using its recursive algorithm,the storage and computational requirements are reduced,and also it can be applicable to the ill-conditioned cases. Simulation demonstrates the superior convergence performance of its recur sive algorithm compared with the back propagation routine