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人工神经网络BP算法的改进和结构的自调整
引用本文:刘光中,李晓峰.人工神经网络BP算法的改进和结构的自调整[J].运筹学学报,2001,5(1):81-88.
作者姓名:刘光中  李晓峰
作者单位:四川大学管理科学与工程系,
摘    要:本文解决了BP神经网络结构参数和学习速率的选取问题,并对传统的BP算法进行了改进,提出了BP神经网络动态全参数自调整学习算法,又将其编制成计算机程序,使得隐层节点和学习速率的选取全部动态实现,减少了人为因素的干预,改善了学习速率和网络的适应能力。计算结果表明:BP神经网络动态全参数自调整算法较传统的方法优越。训练后的神经网络模型不仅能准确地拟合训练值,而且能较精确地预测未来趋势。

关 键 词:人工神经网络  BP算法  学习速率  自组织方法  自调整学习算法  BP神经网络  预测模型
修稿时间:1999年7月30日

The Improvement of BP Algorithm and Self-Adjustment of Structural Parameters (Chinese)
GUANGZHONG LIU,XIAOFENG LI.The Improvement of BP Algorithm and Self-Adjustment of Structural Parameters (Chinese)[J].OR Transactions,2001,5(1):81-88.
Authors:GUANGZHONG LIU  XIAOFENG LI
Abstract:This paper resolves the problem of selecting structural parameters, learning rate and improves BP algorithm of artificial neural network, the self-adjusting algorithm of all parameters has been proposed for the back-propagation learning, and programmed a C language procedure. It can make the selection of hidden layer units and learning rate easily in the course of training, reduce external interference and improve the adaptive ability of learning rate and neural network. Our conclusion shows that the self-adjusting BP algorithm of all parameters is superior to the statistical modelings approach, the model of artificial neural network in tracing can not only exactly imitate training valuation but also make prediction accurately.
Keywords:artificial neural network  BP algorithm  Self-adjustment  Group Method of Data Handling  
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