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

孙圣和; 黄远灿

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

更新时间：2025-12-08

- Modified Nonlinear Least-Square Algorithm for Training Multilayer Feedforward Neural Networks
- Acta Electronica Sinica Issue 1, (1997)
- 作者机构：
  
  哈尔滨工业大学无线电工程系
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183
- Published：1997
- 稿件说明：
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[1]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(01):124-127.

孙圣和, 黄远灿. Modified Nonlinear Least-Square Algorithm for Training Multilayer Feedforward Neural Networks[J]. Acta Electronica Sinica, 1997, (1).
[1]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(01):124-127. DOI：

孙圣和, 黄远灿. Modified Nonlinear Least-Square Algorithm for Training Multilayer Feedforward Neural Networks[J]. Acta Electronica Sinica, 1997, (1). DOI：

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．

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

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