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Appl. Sci. 2016, 6(6), 171; doi:10.3390/app6060171

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

1
School of Mechatronic Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
2
School of Information and Electrical Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editor: César M. A. Vasques
Received: 18 April 2016 / Revised: 20 May 2016 / Accepted: 1 June 2016 / Published: 7 June 2016
View Full-Text   |   Download PDF [2075 KB, uploaded 7 June 2016]   |  

Abstract

In order to accurately evaluate the health condition of a shearer, a hybrid prediction method was proposed based on the integration of a fuzzy neural network (FNN) and improved particle swarm optimization (IPSO). The parameters of FNN were optimized by the use of PSO, which was coupled with a premature judgment and mutation mechanism to increase the convergence speed and enhance the generalization ability. The key technologies are elaborated and the flowchart of the proposed approach was designed. Furthermore, an experiment example was carried out and the comparison results indicated that the proposed approach was feasible and outperforms others. Finally, a field application example in coal mining face was demonstrated to specify the effect of the proposed system. View Full-Text
Keywords: shearer health condition evaluation; fuzzy neural network; particle swarm optimization shearer health condition evaluation; fuzzy neural network; particle swarm optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Si, L.; Wang, Z.; Liu, Z.; Liu, X.; Tan, C.; Xu, R. Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm. Appl. Sci. 2016, 6, 171.

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