基于传递函数自我优化的BP网络算法改进

曲朝阳; 计超

曲朝阳,计超.基于传递函数自我优化的BP网络算法改进[J].电测与仪表,2014,51(11):.
quzhaoyang,jichao.BP neural network algorithm improvement based on transfer function self-optimization[J].Electrical Measurement & Instrumentation,2014,51(11):.

基于传递函数自我优化的BP网络算法改进

BP neural network algorithm improvement based on transfer function self-optimization

DOI：

中文关键词: 传递函数自我优化神经网络风电功率预测 BP算法

英文关键词:transfer function self-optimization,neural network,wind power rediction,BP algorihtm

基金项目:国家自然科学基金资助项目（51277023）国家自然科学基金资助项目（51077010）吉林省科技发展计划重点支撑项目（20120338）

作者中文名	作者英文名	单位
曲朝阳	quzhaoyang	东北电力大学信息工程学院
计超	jichao	东北电力大学信息工程学院

摘要点击次数: 1561

中文摘要:

目前使用比较普遍的优化方法对BP算法改进之后,改进的BP神经网络预测过程都存在复杂程度变大、更加消耗人力资源等缺陷。针对这些缺陷,本文提出一种传递函数自我优化算法来改进神经网络。然后将改进的网络应用到风电功率预测中,以东北某风电场一段时间的风电运行数据作为实验样本,分别采用传统BP神经网络和改进的BP神经网络进行预测分析。仿真结果证明,改进之后的BP神经网络不仅有更快的收敛速度,还有更加精确的预测结果,并且不需要认为干预整个预测过程。极大提高了网络的预测能力和效率。

英文摘要:

Now after using common optimization to improve the BP algorithm,the improved BP neural network almost become very complex and consume more human resources during the prediction process.To solve these shortcomings,this paper presents a transfer function self-optimization algorithm to improve the neural network,then apply the improved network to wind power prediction.Take a period time of operating data in a northeast wind farm as the experimental samples to analyze prddiction outcomes by using both traditional and improved BP neural network.Prediction results show that the improved BP neural network not only enhances the convergence reat,but also prediction accuracy.

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