A Novel Adjustment Method for Shearer Traction Speed through Integration of T-S Cloud Inference Network and Improved PSO. (23rd November 2014)

• Record Type: Journal Article
• Authors: Si, Lei; Wang, Zhongbin; Liu, Xinhua; Yang, Yinwei; Zhang, Lin
• Other Names: Oweiss Karim G. Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2014(2014)
• Publication Date: 2014-11-23
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2014/865349
• Languages: English
• ISSNs: 1687-5265

Abstract

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.