文章基本信息

标题：The Load Forecasting Model Based on Bayes-GRNN
本地全文：下载
作者：Li, Yanmei ; Wang, Jingmin
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2012
卷号：7
期号：6
页码：1273-1280
DOI：10.4304/jsw.7.6.1273-1280
语种：English
出版社：Academy Publisher
摘要：Comparison with the classical BP neural network, the generalized regression neural network requires not periodic training process but a smoothing parameter. The model has steady and fast speed, and meanwhile, the connection weight of different neurons is not necessary to be adjusted in the training process. The paper establishes the index system of GRNN forecasting model, and then uses Bayes theory to reduce them, which will be inputting variables of GRNN model. The method is testified to get higher speed and accuracy by simulation of actual data and comparison to classical BP neural network.
关键词：Bayes; load forecasting; generalized regression neural network