首页 | 本学科首页   官方微博 | 高级检索  
     

智能预报模式与水文中长期智能预报方法
引用本文:陈守煜,郭 瑜,王大刚. 智能预报模式与水文中长期智能预报方法[J]. 中国工程科学, 2006, 8(7): 30-35
作者姓名:陈守煜  郭 瑜  王大刚
作者单位:大连理工大学土木水利学院,辽宁,大连,116024
摘    要:建立了以模糊优选、BP神经网络及遗传算法有机结合的智能预报模式与方法。在应用该方法进行中长期水文智能预报时,首先选取训练样本的数量,根据预报因子与预报对象的相关关系得到相对隶属度矩阵;再将其作为BP神经网络输入值以训练连接权重;最后将得到的连接权重值用于预报检验。计算结果表明,智能预报模式与方法的运行速度、精度及稳定性都达到了实际应用的要求。

关 键 词:模糊优选  BP神经网络  遗传算法  智能预报模式  中长期水文智能预报
文章编号:1009-1742(2006)07-0030-06
收稿时间:2005-05-08
修稿时间:2006-06-28

Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting
chenshouyu,guoyu and wangdagang. Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting[J]. Engineering Science, 2006, 8(7): 30-35
Authors:chenshouyu  guoyu  wangdagang
Abstract:Intelligent calculating tools such as fuzzy optimization approaches, BP neural network and genetic algorithm are proven to be efficient when applied individually to a variety of problems. Recently, there has been a growing interest in combing all these approaches, and then, in this paper, the author organically synthesizes fuzzy optimal selection, BP neural network and genetic algorithm and establishes intelligent forecasting mode and method. When illustrating the method by an application to forecast mid and long term hydrological process of Yamadu Hydrographic Station at Yili River in Xinjiang, China, the author first selects the amount of training samples, and gets relative membership degree matrix according to the correlation of forecasting factors and forecasting objective, then takes the matrix as input of BP neural network to train link-weights, and finally, uses gained link-weight values to verify forecasting. The results are highly promising and show that the operation speed, precision and stability of intelligent forecasting mode presented in this paper can completely meet actual requirement.
Keywords:fuzzy optimal selection   BP neural network   genetic algorithm   intelligent forecasting mode  mid and long term intelligent hydrological forecasting
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《中国工程科学》浏览原始摘要信息
点击此处可从《中国工程科学》下载全文
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