摘要
垃圾发电厂选址的影响因素多、社会影响大。基于主成分分析法和改进反向传输(back propagation,BP)神经网络,提出一种垃圾发电厂选址优化算法。算法采用主成分分析选取学习样本,使少量样本包含尽可能多的样本特性;应用Levenberg-M arquardt反向传播算法对神经网络进行训练,加快了神经网络训练速度。
The site selection of refuse incineration power plant has many influencing factor and is of severe social impact.In this paper,an optimizing algorithm for site selection of refuse incineration power plant is provided based on principal component analysis and back propagation（BP） neural network.The samples are selected by principal component analysis,as a result that a few samples can reflect as many sample characteristics as possible.The neural network is trained by Levenberg-Marquardt BP algorithm,and the training speed is improved.
出处
《电力建设》
2011年第6期67-69,共3页
Electric Power Construction
关键词
垃圾发电厂
选址
神经网络
主成分分析法
refuse incineration power plant
site selection
neural network
principal component analysis
