[1]丁华福 柴琳.基于Bagging算法和遗传BP神经网络的负荷预测[J].计算机技术与发展,2011,(05):107-110.
　DING Hua-fu,CHAI Lin.Load Forecasting Based on Bagging Method and GABP Neural Network[J].,2011,(05):107-110.
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基于Bagging算法和遗传BP神经网络的负荷预测()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年05期
页码:
107-110
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Load Forecasting Based on Bagging Method and GABP Neural Network
文章编号:
1673-629X（2011）05-0107-04
作者:
丁华福 柴琳
哈尔滨理工大学计算机科学与技术学院
Author(s):
DING Hua-fu;  CHAI Lin
Sch. of Computer Sci. and Tech. , Harbin Univ. of Sci. and Tech
关键词:
BP神经网络; 遗传算法; Bagging方法; 负荷预测
Keywords:
BP neural network ;  genetic algorithm ;  Bagging ;  load forecast
分类号:
TP183
文献标志码:
A
摘要:
负荷预测是电力规划的基础，传统的神经网络预测方法存在对初始网络权值设置敏感、收敛的速度慢、容易陷入局部极小值等缺点。文中引入遗传算法先对神经网络的初始值进行优化，再通过神经网络进行学习和训练，得出的结果再经Bagging方法集成，目的是提高其准确率。通过Matlab仿真进行实验，结果表明，基于Bagging算法集成遗传神经网络，能够克服传统BP神经网络的缺点，可较快收敛义小易陷入到局部极值中，具有较强的泛化能力，同时也大大提高了网络的预测精度
Abstract:
The load forecast is the electric power plan foundation. However, there are lots of disadvantages in the traditional neural net- work prediction ways, including be sensitive to the initial network weights, easy to run into the local minimum point, etc. Brings forward genetic algorithm to the BP neural network, optimizing the initial network weights. In order to improve the accuracy, use the Bagging method integrated the results. Through the simulation experiment on Matlab, found out that by our research not only the Bagging method and genetic neural network can avoid the disadvantages in the traditional BP network and inherit its good learning and training a- bilities, but also have stronger generalization ability, and improve the prediction precision

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备注/Memo

备注/Memo:
国家自然科学基金项目（60736014）丁华福（1962-），男，黑龙江哈尔滨人，硕士研究生导师，研究员，研究方向为数据挖掘、数据库
更新日期/Last Update: 1900-01-01