A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network

作     者：Victor Amoako Temeng Yao Yevenyo Ziggah Clement Kweku Arthur Victor Amoako Temeng;Yao Yevenyo Ziggah;Clement Kweku Arthur

作者机构：Department of Mining EngineeringFaculty of Mineral Resources TechnologyUniversity of Mines and TechnologyTarkwaWestern RegionGhana Department of Geomatic EngineeringFaculty of Mineral Resources TechnologyUniversity of Mines and TechnologyTarkwaWestern RegionGhana 

出 版 物：《International Journal of Mining Science and Technology》 (矿业科学技术学报（英文版）)

年 卷 期：2020年第30卷第5期

页      面：683-689页

核心收录：

学科分类：12[管理学] 081901[工学-采矿工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0819[工学-矿业工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the Ghana National Petroleum Corporation(GNPC)through the GNPC Professorial Chair in Mining Engineering at the University of Mines and Technology(UMaT),Ghana The authors thank the Ghana National Petroleum Corporation(GNPC)for providing funding to support this work through the GNPC Professorial Chair in Mining Engineering at the University of Mines and Technology(UMaT),Ghana 

主　　题：Air overpressure Artificial intelligence Emotional neural network Blasting Mining 

摘      要：Blasting is the live wire of mining and its operations,with air overpressure(AOp)recognised as an end product of *** is known to be one of the most important environmental hazards of *** research in this area of mining is required to help improve on safety of the working *** of previous studies has shown that many empirical and artificial intelligence(AI)methods have been proposed as a forecasting *** an alternative to the previous methods,this study proposes a new class of advanced artificial neural network known as brain inspired emotional neural network(BIENN)to predict *** proposed BI-ENN approach is compared with two classical AOp predictors(generalised predictor and McKenzie formula)and three established AI methods of backpropagation neural network(BPNN),group method of data handling(GMDH),and support vector machine(SVM).From the analysis of the results,BI-ENN is the best by achieving the least RMSE,MAPE,NRMSE and highest R,VAF and PI values of 1.0941,0.8339%,0.1243%,0.8249,68.0512%and 1.2367 respectively and thus can be used for monitoring and controlling AOp.