摘要
针对神经网络模型预测结果的随机性,构建了一种紧致性小波神经网络工具箱。该方法将小波函数移植到BP网络隐层,并采用一种随机确定状态命令获得确定的预测结果。与编程实现的小波神经网络和BP网络比较,该方法适合于大批量数据训练,对数据样本的适应能力和鲁棒性强,尤其对高频随机时间序列有更好的适应能力,具有预测结果确定及实用性强等特点,可显著提高模型的训练速度、预测精度和预测效率。基于小波包变换和小波神经网络的瓦斯涌出量预测实验证明了所提方法的有效性。
Concerning the random prediction results of the neural network model,a compact wavelet neural network was constructed.The method transferred the wavelet function into the hidden layer of the Back-Propagation（BP） network and made use of a random certain state command to obtain the definite prediction results.Compared with the wavelet neural network realized by programming and BP network,this method is suitable for mass data training and has such advantages as strong adaptability and robustness for data samples,especially has better adaptability for high frequency stochastic time series,and has characteristics of determined predicted results,powerful practicability and so on.It can obviously improve the training speed,prediction accuracy and prediction efficiency of the model.Its efficiency has been proved by the gas emission prediction experiment of wavelet packet transformation and wavelet neural network.
出处
《计算机应用》
CSCD
北大核心
2013年第4期1001-1005,共5页
journal of Computer Applications
基金
国家安全生产监督管理总局安全生产科技发展指导性计划项目(06-472)
河北省教育厅科学技术研究项目(Z2006439)
关键词
小波神经网络
工具箱
小波包
瓦斯涌出量
预测
wavelet neural network
toolbox
wavelet packet
gas emission
prediction
