首页 | 本学科首页   官方微博 | 高级检索  
     

基于卡尔曼滤波技术的人工神经网络权重估算及应用
引用本文:覃光华,王顺久,缪韧.基于卡尔曼滤波技术的人工神经网络权重估算及应用[J].四川大学学报(工程科学版),2008,40(4):25-28.
作者姓名:覃光华  王顺久  缪韧
作者单位:四川大学,水利水电学院,四川,成都,610065
基金项目:中国气象局成都高原气象研究所基本科研专项项目
摘    要:为改进神经网络模型算法,将神经网络技术与卡尔曼滤波技术进行耦合.在样本训练过程中,将卡尔曼滤波递推算法用于神经网络权重的训练,然后用训练得到的权重进行检验.文中以岷江上游段紫坪埔水文站的流量预报为实例,并与单一的神经网络模型以及卡尔曼滤波模型进行了比较.应用结果表明,卡尔曼技术用于神经网络权重估算,可改善水文预报精度.

关 键 词:神经网络  卡尔曼滤波  权重
收稿时间:6/5/2007 12:00:00 AM

Estimation and Application of Artificial Neural Networks' Weight Based on Kalman Filter Technique
QIN Guang-hua,WANG Shun-jiu,MIAO Ren.Estimation and Application of Artificial Neural Networks'''' Weight Based on Kalman Filter Technique[J].Journal of Sichuan University (Engineering Science Edition),2008,40(4):25-28.
Authors:QIN Guang-hua  WANG Shun-jiu  MIAO Ren
Abstract:The paper combines artificial neural networks (ANNs) with Kalman filter real time adjustment technique in order to improve traditional ANNs model. The weights are trained by Kalman filter real time adjustment technique in the process of sample training, and then the weights are used for check.One case is flow forecasting for upper reach of Minjing River at Zipingpu station by using the method proposed in the paper, and the results are compared with single ANNs model and single Kalman filter model. The results show if Kalman filter technique is used in estimating networks weights,hydrologic forecast accuracy may be improved.
Keywords:
本文献已被 维普 万方数据 等数据库收录!
点击此处可从《四川大学学报(工程科学版)》浏览原始摘要信息
点击此处可从《四川大学学报(工程科学版)》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