庞清乐;

• 1:山东工商学院信息与电子工程学院

摘要(Abstract)：

神经网络具有万能逼近能力,在模式识别、模型预测和数据挖掘等领域得到了广泛应用。但是,神经网络在被逼近非线性函数峰值处的误差较大,当峰值两侧的斜率差较大时误差更大。提出了基于粗糙集理论的改进神经网络算法,并将其应用于短期负荷预测。将当前时间间隔负荷、前一时间间隔负荷、当前时间间隔和前一时间间隔的负荷差和当前时间分别作为神经网络预测模型的输入,将下一时间间隔的预测负荷作为神经网络的输出,利用粗糙集理论对神经网络预测模型输出的预测负荷进行补偿,使预测精度更高。仿真结果表明,该方法能显著提高函数的预测精度。

关键词(KeyWords)： 负荷预测;神经网络;粗糙集理论

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金资助项目(50777040);; 中国博士后科学基金资助项目(20090461204);; 山东省博士后创新项目专项资金资助项目(200903066);; 山东省高等学校科技计划项目(J09LG09)。~~

作者(Author): 庞清乐;

Email:

DOI: 10.13335/j.1000-3673.pst.2010.12.027

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