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Dynamic prediction of gas emission based on wavelet neural network toolbox 被引量:4

Dynamic prediction of gas emission based on wavelet neural network toolbox
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摘要 This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network (WNN) toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.
出处 《Journal of Coal Science & Engineering(China)》 2013年第2期174-181,共8页 煤炭学报(英文版)
关键词 dynamic prediction gas emission wavelet neural network TOOLBOX prediction model 神经网络工具箱 小波神经网络 动态预测 气体排放量 瓦斯涌出量预测 时间间隔 特征提取 滑动窗口
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