Evidence map›Paper›PMID 25506358›Full record

ArticleComputational intelligence and neuroscience2014

A novel adjustment method for shearer traction speed through integration of T-S cloud inference network and improved PSO.

Lei Si, Zhongbin Wang, Xinhua Liu, Yinwei Yang, Lin Zhang

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2014. 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

5 authors.

Lei SiSchool of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
Zhongbin WangSchool of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
Xinhua LiuSchool of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China ; Xuyi Mine Equipment and Materials R&D Center, China University of Mining & Technology, Huai'an 211700, China.ORCID 0000-0002-8632-6532
Yinwei YangSchool of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.
Lin ZhangSchool of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 221116, China.ORCID 0000-0003-0866-7937

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to efficiently and accurately adjust the shearer traction speed, a novel approach based on Takagi-Sugeno (T-S) cloud inference network (CIN) and improved particle swarm optimization (IPSO) is proposed. The T-S CIN is built through the combination of cloud model and T-S fuzzy neural network. Moreover, the IPSO algorithm employs parameter automation adjustment strategy and velocity resetting to significantly improve the performance of basic PSO algorithm in global search and fine-tuning of the solutions, and the flowchart of proposed approach is designed. Furthermore, some simulation examples are carried out and comparison results indicate that the proposed method is feasible, efficient, and is outperforming others. Finally, an industrial application example of coal mining face is demonstrated to specify the effect of proposed system.

Indexed as

Computer SimulationMechanical PhenomenaModels, TheoreticalNeural Networks, ComputerTractionAlgorithmsHumans

Identifiers

PMID25506358
PMCPMC4259143

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.