首页 | 本学科首页   官方微博 | 高级检索  
     

改进的非线性最小二乘算法训练多层前馈神经网络
引用本文:孙圣和 黄远灿. 改进的非线性最小二乘算法训练多层前馈神经网络[J]. 电子学报, 1997, 25(1): 124-127
作者姓名:孙圣和 黄远灿
作者单位:哈尔滨工业大学无线电工程系
摘    要: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,包括地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的BP算法更好的收敛性能。

关 键 词:非线性 最小二乘算法 准则函数 前馈神经网络

Modified Nonlinear Least-Square Algorithm for Training Multilayer Feedforward Neural Networks
Abstract: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
Keywords:Nonlinear least-square algorithm  Criterion  Multilayer feedforward neural network  Regularization  
本文献已被 CNKI 维普 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号