Gas emission prediction model of working face based on chaos immune particle swarm optimizations and generalized regression neural network

doi:10.11772/j.issn.1001-9081.2014.11.3348

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3348-3352.DOI: 10.11772/j.issn.1001-9081.2014.11.3348

Previous Articles Next Articles

Gas emission prediction model of working face based on chaos immune particle swarm optimizations and generalized regression neural network

WANG Yuhong¹,FU Hua¹,HOU Fujian¹,ZHANG Yang²

1. School of Electrical and Control Engineering, Liaoning Technical University, Huludao Liaoning 125105,China;
2. School of Electronic and Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China

Received:2014-05-22 Revised:2014-07-11 Online:2014-12-01 Published:2014-11-01
Contact: WANG Yuhong

基于混沌免疫粒子群优化和广义回归神经网络的回采工作面瓦斯涌出量预测模型

王雨虹¹,付华¹,侯福营¹,张洋²

1. 辽宁工程技术大学电气与控制工程学院,辽宁葫芦岛 125105
2. 辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛 125105

通讯作者: 王雨虹
作者简介:
王雨虹(1979-),女,辽宁阜新人,讲师,博士研究生,主要研究方向:智能算法、智能检测监控;付华(1962-),女,辽宁阜新人,教授,博士生导师,主要研究方向:信息处理、智能检测、智能控制;侯福营(1987-),男,山东菏泽人,硕士研究生,主要研究方向:机器学习、智能优化算法;张洋(1979-),男,辽宁葫芦岛人,讲师,博士研究生,主要研究方向:计算机系统仿真。
基金资助:
国家自然科学基金资助项目;辽宁省科技攻关项目

Abstract

Abstract:

To improve the accuracy and efficiency of absolute gas emission prediction, a new algorithm based on Chaos Immune Particle Swarm Optimization (CIPSO) and General Regression Neural Network (GRNN) was proposed. In this algorithm, CIPSO was employed to dynamically optimize the smooth factor of GRNN to reduce the impact of artificial factors in GRNN model construction, and then the optimized network was adopted to establish gas emission prediction model. The simulation experiment results on gas emission data of a coal mine show that the model is of faster convergence and higher prediction accuracy than other prediction models based on BP and Elman neural network. It is proved that the proposed method is feasible and effective.

摘要：

为提高回采工作面绝对瓦斯涌出量预测的精度和效率,提出了将混沌免疫粒子群优化(CIPSO)算法与广义回归神经网络(GRNN)相耦合的绝对瓦斯涌出量预测模型。该方法采用CIPSO对GRNN的光滑因子进行动态优化调整,减少了人为因素对GRNN网络输出结果的影响,并采用优化后的网络建立瓦斯涌出量预测模型。通过对某煤矿瓦斯涌出量数据的仿真实验结果表明:基于CIPSO-GRNN的回采工作面绝对瓦斯涌出量模型比BP神经网络、Elman网络预测模型具有更好的预测精度和收敛速度,证明了该方法的有效性和可行性。

CLC Number:

TP183

WANG Yuhong FU Hua HOU Fujian ZHANG Yang. Gas emission prediction model of working face based on chaos immune particle swarm optimizations and generalized regression neural network[J]. Journal of Computer Applications, 2014, 34(11): 3348-3352.

王雨虹付华侯福营张洋. 基于混沌免疫粒子群优化和广义回归神经网络的回采工作面瓦斯涌出量预测模型[J]. 计算机应用, 2014, 34(11): 3348-3352.

References

[1]FUA H, SHAO L. Mine gas disaster features mining and the fusion prediction[M]. Beijing: Science Press,2011.(付华,邵良杉.煤矿瓦斯灾害特征挖掘与融合预测[M].北京:科学出版社,2011).
[2]HE L, SHI S, SONG Y, et al. Complexity and measurement of complex degree of gas gush heading faces of coal mine[J]. Journal of China Coal Society, 2008,33(5):547-550.(何利文,施式亮,宋译,等.回采工作面瓦斯涌出的复杂性及其度量[J].煤炭学报,2008,33(5):547-550.)
[3]PAN Y, DENG Y, ZHANG Q, et al. Gas emission prediction model based on QPSO-RBF[J]. China Safety Science Journal, 2012, 22(12): 29-34.(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).
[4]FU H, XIE S, XU Y, et al. Study on MPSO-WLS-SVM-based mine gas emission prediction model[J].China Safety Science Journal,2013,23(5):56-61.(付华,谢森,徐耀松,等.基于MPSO-WLS-SVM的矿井瓦斯涌出量预测模型研究[J].中国安全科学学报,2013,23(5):56-61.)
[5]FU H, JIANG W, SHAN X. Study on coupling algorithm on coal mine gas emission forecast model[J]. Journal of China Coal Society,2012,37(4):654-658.(付华,姜伟,单欣欣.基于耦合算法的煤矿瓦斯涌出量预测模型研究[J].煤炭学报,2012,37(4):654-658.)
[6]XIAO P, LI S, SONG Y, et al. Prediction and grey-model building for gas emission in coal mines[J]. Journal of Mining and Safety Engineering, 2009,26(3):317-321.(肖鹏,李树刚,宋莹,等.瓦斯涌出量的灰色建模及其预测[J].采矿与安全工程学报,2009,26(3):317-321.)
[7]JIA Y, LU Q, SHANG Y. Rockburst prediction using particle swarm optimization algorithm and general regression neural network[J]. Chinese Journal of Rock Mechanics and Engineering, 2013,32(2):343-348.(贾义鹏,吕庆,尚岳.基于粒子群算法和广义回归神经网络的岩爆预测[J].岩土力学与工程学报,2013,32(2):343-348.)
[8]SU Y, SHEN J, ZHANG D, et al. Application of networks and improved PSO algorithms to earthquake prediction[J]. Journal of Computer Applications, 2011,31(7):1793-1796.(苏义鑫,沈俊,张丹红,等.神经网络和改进粒子群算法在地震预测中的应用[J].计算机应用,2011,31(7):1793-1796.)
[9]FU H, QIAO D, CHI J. CIPSO-ENN coupling algorithm for nonlinear dynamic system 〖HJ1.8mm〗parameter identification[J]. Journal of Xi'an Jiaotong University,2011,45(2):49-53.(付华,乔德浩,池继辉.一种非线性系统参数辨识的耦合算法研究[J].西安交通大学学报,2011,45(2):49-53.)
[10]LIU J, GAO Y. Chaos particle swam optimization algorithm [J]. Journal of Computer Applications,2008,28(2):322-325.(刘军民,高岳林.混沌粒子群优化算法[J].计算机应用,2008,28(2):322-325.)
[11]SHI S, SONG Y, HE L, et al. Research on determination of chaotic characteristics of gas gush based on time series in excavation working face of coal mine[J]. Journal of China Coal Society, 2006,31(6):701-705.(施式亮,宋译,何利文,等.矿井掘进工作面瓦斯涌出量混沌特性判别研究[J].煤炭学报,2006,31(6):701-705.)
[12]ZHU H, CHANG W, ZHANG B. Different-source gas emission prediction model of working face based on BP artificial neural network and its application[J]. Journal of China Coal Society, 2007,32(5):504-508.(朱红青,常文杰,张彬.回采工作面瓦斯涌出BP神经网络分源预测模型及应用[J].煤炭学报,2007,32(5):504-508.)
[13]SUN L, YANG S. Prediction for gas emission quantity of the working face based on LS-SVM[J]. Journal of China Coal Society,2008,33(12):1377-1380.(孙林,杨世元.基于LS-SVM的回采工作面瓦斯涌出量预测[J].煤炭学报,2008,33(12):1377-1380.)

[1]	Yanran SHEN, Xin WEN, Jinhao ZHANG, Shuai ZHANG, Rui CAO, Baolu GAO. fMRI brain age prediction model with lightweight multi-scale convolutional network [J]. Journal of Computer Applications, 2024, 44(12): 3949-3957.
[2]	. Equivariant graph neural network-based simulator for lagrangian particle flow [J]. Journal of Computer Applications, 0, (): 0-0.
[3]	. Multi-scale Information Fusion Time Series Long-term Forecasting Model Based on Neural Network [J]. Journal of Computer Applications, 0, (): 0-0.
[4]	. Multi-scale Decorrelation Graph Convolutional Network [J]. Journal of Computer Applications, 0, (): 0-0.
[5]	. Object detection uncertainty measurement scheme based on guide to the expression of uncertainty in measurement [J]. Journal of Computer Applications, 0, (): 0-0.
[6]	Nengbing HU, Biao CAI, Xu LI, Danhua CAO. Graph classification method based on graph pooling contrast learning [J]. Journal of Computer Applications, 2024, 44(11): 3327-3334.
[7]	Fu LIN, Jiasheng SHI, Ze GAO, Zunkang CHU, Qiongmin MA, Haiyan YU, Weixiong RAO. Physical system simulation based on deep representation learning for 3D geometric features [J]. Journal of Computer Applications, 2024, 44(11): 3548-3555.
[8]	Yiyang FAN, Yang ZHANG, Shang ZENG, Yu ZENG, Maoli FU. Multivariate long-term series forecasting model based on decomposition and frequency domain feature extraction [J]. Journal of Computer Applications, 2024, 44(11): 3442-3448.
[9]	Wenbo LIU, Lianfei YU, Dongmei XIE, Chuang CAI, Zhijian QU, Chongguang REN. Long-term prediction model of time series based on multi-scale feature fusion [J]. Journal of Computer Applications, 2024, 44(11): 3435-3441.
[10]	. Medical image segmentation network with content-guided multi-angle feature fusion [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	WANG Xiang, CHEN Zhixiang, MAO Guojun. Multivariate time series prediction method combining local and global correlation#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	Wenjuan JIANG, Yi GUO, Jiaojiao FU. Reasoning question answering model of complex temporal knowledge graph with graph attention [J]. Journal of Computer Applications, 2024, 44(10): 3047-3057.
[13]	Hua HUANG, Ziyi YANG, Xiaolong LI, Chuang LI. Predictive business process monitoring method based on concept drift [J]. Journal of Computer Applications, 2024, 44(10): 3167-3176.
[14]	Rui ZHANG, Junming PAN, Xiaolu BAI, Jing HU, Rongguo ZHANG, Pengyun ZHANG. Agent model for hyperparameter self-optimization of deep classification model [J]. Journal of Computer Applications, 2024, 44(10): 3021-3031.
[15]	. Pedestrian trajectory prediction based on graph convolutional network and endpoint induction [J]. Journal of Computer Applications, 0, (): 0-0.

Gas emission prediction model of working face based on chaos immune particle swarm optimizations and generalized regression neural network

基于混沌免疫粒子群优化和广义回归神经网络的回采工作面瓦斯涌出量预测模型

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics