Evidence map›Paper›PMID 34584154›Full record

ArticleScientific reports2021

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

Meng Wang, Caiwang Tai, Qiaofeng Zhang, Zongwei Yang, Jiazheng Li, Kejun Shen

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Meng WangCollege of Mining Engineering, Liaoning Technical University, Fuxin, 123000, China.
Caiwang TaiCollege of Mining Engineering, Liaoning Technical University, Fuxin, 123000, China. taicaiwang@126.com.
Qiaofeng ZhangCollege of Mining Engineering, Liaoning Technical University, Fuxin, 123000, China.
Zongwei YangCollege of Mining Engineering, Liaoning Technical University, Fuxin, 123000, China.
Jiazheng LiCollege of Mining Engineering, Liaoning Technical University, Fuxin, 123000, China.
Kejun ShenCollege of Mining Engineering, Liaoning Technical University, Fuxin, 123000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID34584154
PMCPMC8478949

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.