杜鹃;

• 1:南洋理工大学

摘要(Abstract)：

将改进的TSK型模糊神经网络(fuzzy neural network,FNN)应用于短期负荷预测。该FNN由椭圆基函数构成神经元的中心和宽度参数,并且具有以下特征:网络结构和参数可自动并同时进行调整,不需提前分割输入空间,也不需提前选择网络初始参数;模糊规则在学习过程中可动态增删,不需采用迭代算法即可快速生成。这种模糊规则可动态增删的模糊神经网络(growing and pruning fuzzy neural network,GPFNN)简单有效,可以降低网络的复杂性,加快网络的学习速度。使用EUNITE竞赛数据作测试数据对上述GPFNN方法进行测试,结果表明采用该方法进行短期负荷预测时可获得较高的准确率。

关键词(KeyWords)： 动态模糊神经网络;短期负荷预测;椭圆基函数;模糊规则;EUNITE竞赛数据

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 杜鹃;

Email:

DOI: 10.13335/j.1000-3673.pst.2010.04.007

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