期刊导航

论文摘要

基于禁忌搜索的前向神经网络的径流预测

Prediction of Runoff Based on Forward Neural Network Optimized by Taboo Search

作者:李祚泳(成都信息工程学院,四川 成都 610041);汪嘉杨(成都信息工程学院,四川 成都 610041);邬敏(成都信息工程学院,四川 成都 610041)

Author:(Chengdu Univ. of Information Technol, Chengdu 610041, China);(Chengdu Univ. of Information Technol, Chengdu 610041, China);(Chengdu Univ. of Information Technol, Chengdu 610041, China)

收稿日期:2007-06-27          年卷(期)页码:2008,40(4):7-11

期刊名称:工程科学与技术

Journal Name:Advanced Engineering Sciences

关键字:禁忌搜索;神经网络;径流;预测

Key words:taboo search;neural network;runoff;prediction

基金项目:国家自然科学基金资助项目(50779042;50739002);成都信息工程学院发展基金资助项目(CSRF200401)

中文摘要

BP算法具有学习效率低、收敛速度慢和易陷入局部极值的局限,针对此不足,提出将禁忌搜索算法与前向神经网络相结合方法,即采用禁忌技术指导神经网络的参数调整,从而使参数调整过程中避免了局部邻域搜索,具有收敛于全局最优的能力。该方法应用于新疆伊梨河雅马渡站年径流量的预测建模,并与用传统BP算法的预测建模相比较,结果表明:基于禁忌搜索的前向神经网络优化算法不仅提高了算法效率,而且预测精度也有一定的改善。

英文摘要

In order to avoid the shortcoming of lower learning efficiency, slow rate of convergence and tend to trap into local extremum of BP algorithm, the technology combined with taboo search (TS) in forward neural network is proposed for the parameters adjustment, namely parameters of neural network are guided by TS to avoid searching in local neighbor region. The method is applied to runoff prediction in Yama Du station of Yili River in Xinjiang, and is compared with traditional BP algorithm.Results show that forward neural network based on TS not only increases the algorithm efficiency, but also improves the forecasting precision.

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