Wavelet Packet Neural Network Prediction Method Applied in the Gas Emission

PENG Xiao-hua; LIU Li-qiang

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Wavelet Packet Neural Network Prediction Method Applied in the Gas Emission

更新时间：2026-09-04

- Wavelet Packet Neural Network Prediction Method Applied in the Gas Emission
- Vol. 33, Issue 3, Pages: 130-134(2016)
- 作者机构：
  
  1. 辽宁工程技术大学机构基础教学部,辽宁,葫芦岛,125105
  2. 辽宁工程技术大学电气与控制工程学院,辽宁,葫芦岛,125105
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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彭晓华, 刘利强. 小波包神经网络预测方法在瓦斯涌出量中的应用[J]. 微电子学与计算机, 2016, 33(3): 130-134.

PENG Xiao-hua, LIU Li-qiang. Wavelet Packet Neural Network Prediction Method Applied in the Gas Emission[J]. 2016, 33(3): 130-134.
彭晓华, 刘利强. 小波包神经网络预测方法在瓦斯涌出量中的应用[J]. 微电子学与计算机, 2016, 33(3): 130-134. DOI：

PENG Xiao-hua, LIU Li-qiang. Wavelet Packet Neural Network Prediction Method Applied in the Gas Emission[J]. 2016, 33(3): 130-134. DOI：

摘要

针对煤矿井下回采工作面瓦斯积聚和瓦斯超限等严重问题

将小波包神经网络模型引入煤矿瓦斯涌出量预测中.首先由改进小波包变化对采集数据进行分解、重构并提取特征向量

然后输入到基于动态节点生成算法的RBF神经网络模型中训练学习

同时采用删除策略简化该模型

最后通过时频联合仿真验证.结果表明

WP-RBF模型在预测精度及训练误差方面明显优于QPSO-RBF模型

是一种非常适合煤矿瓦斯量预测的有效方法.

Abstract

According to serious problems such as gas accumulation and gas concentration exceeding limits on the working face underground coal mines

Introducing wavelet packet and neural network model in prediction of mine gas emission. First

The collected data decomposition

reconstruction and extracted feature vectors by wavelet packet transform

Then input to generation algorithm based on dynamic node RBF neural network model training and learning

While using the simplified model delete policy. Finally

By joint time-frequency simulation

The results show that WP-IRBF model is far superior to QPSO-RBF model in prediction accuracy and training error and is a very suitable for effective method for prediction of coal mine gas quantity.

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references

Yu-Min PAN, Yong-Hong DENG, Quan-Zhu Zhang. Dynamic prediction of gas emission based on wavelet neural network toolbox[J]. Journal of Coal Science and Engineering, 2013, 2(7): 10-14. DOI: 10

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郭伟, 张栋, 李巨韬, 等.改进型BP神经网络对电容称重传感器的非线性校正[J].传感技术学报, 2012, 25(10): 1354-1360. DOI: 1354

潘玉民, 邓永红, 张全柱, 等.基于QPSO-RBF的瓦斯涌出量预测模型[J].中国安全科学学报, 2012, 22(12): 29-34. DOI: 29

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