Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability

Abstract Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simu...

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Autores principales:	Meng Wang, Caiwang Tai, Qiaofeng Zhang, Zongwei Yang, Jiazheng Li, Kejun Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f690cc6fd6574d81bb66abf6d2a21f19
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spelling	oai:doaj.org-article:f690cc6fd6574d81bb66abf6d2a21f192021-12-02T18:51:36ZApplication of improved and optimized fuzzy neural network in classification evaluation of top coal cavability10.1038/s41598-021-98630-42045-2322https://doaj.org/article/f690cc6fd6574d81bb66abf6d2a21f192021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98630-4https://doaj.org/toc/2045-2322Abstract Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.Meng WangCaiwang TaiQiaofeng ZhangZongwei YangJiazheng LiKejun ShenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Meng Wang Caiwang Tai Qiaofeng Zhang Zongwei Yang Jiazheng Li Kejun Shen Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability
description	Abstract Longwall top coal caving technology is one of the main methods of thick coal seam mining in China, and the classification evaluation of top coal cavability in longwall top coal caving working face is of great significance for improving coal recovery. However, the empirical or numerical simulation method currently used to evaluate the top coal cavability has high cost and low-efficiency problems. Therefore, in order to improve the evaluation efficiency and reduce evaluation the cost of top coal cavability, according to the characteristics of classification evaluation of top coal cavability, this paper improved and optimized the fuzzy neural network developed by Nauck and Kruse and establishes the fuzzy neural network prediction model for classification evaluation of top coal cavability. At the same time, in order to ensure that the optimized and improved fuzzy neural network has the ability of global approximation that a neural network should have, its global approximation is verified. Then use the data in the database of published papers from CNKI as sample data to train, verify and test the established fuzzy neural network model. After that, the tested model is applied to the classification evaluation of the top coal cavability in 61,107 longwall top coal caving working face in Liuwan Coal Mine. The final evaluation result is that the top coal cavability grade of the 61,107 longwall top coal caving working face in Liuwan Coal Mine is grade II, consistent with the engineering practice.
format	article
author	Meng Wang Caiwang Tai Qiaofeng Zhang Zongwei Yang Jiazheng Li Kejun Shen
author_facet	Meng Wang Caiwang Tai Qiaofeng Zhang Zongwei Yang Jiazheng Li Kejun Shen
author_sort	Meng Wang
title	Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability
title_short	Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability
title_full	Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability
title_fullStr	Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability
title_full_unstemmed	Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability
title_sort	application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f690cc6fd6574d81bb66abf6d2a21f19
work_keys_str_mv	AT mengwang applicationofimprovedandoptimizedfuzzyneuralnetworkinclassificationevaluationoftopcoalcavability AT caiwangtai applicationofimprovedandoptimizedfuzzyneuralnetworkinclassificationevaluationoftopcoalcavability AT qiaofengzhang applicationofimprovedandoptimizedfuzzyneuralnetworkinclassificationevaluationoftopcoalcavability AT zongweiyang applicationofimprovedandoptimizedfuzzyneuralnetworkinclassificationevaluationoftopcoalcavability AT jiazhengli applicationofimprovedandoptimizedfuzzyneuralnetworkinclassificationevaluationoftopcoalcavability AT kejunshen applicationofimprovedandoptimizedfuzzyneuralnetworkinclassificationevaluationoftopcoalcavability
_version_	1718377422174289920

Application of improved and optimized fuzzy neural network in classification evaluation of top coal cavability

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