A Novel Adjustment Method for Shearer Traction Speed through Integration of T-S Cloud Inference Network and Improved PSO

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.

An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process

“…As a major coal mining machine, shearer plays a pivotal role in getting high-security and high efficiency of exploitation [ 1 , 2 ]. In order to realize the automated control of shearer, multiple coal-rock interface recognizing and tracking methods [ 3 – 6 ] were proposed, but these methods are not satisfactory because of the poor working conditions of coal mining such as narrow space, high coal dust, low visibility, and large noise [ 7 ]. Memory cutting as a better indirect solving strategy than aforementioned methods has been emphasized [ 8 ].…”

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

“…It can also handle imprecise information through linguistic expressions. For several decades, FNN has attracted much attention and has been applied in many domains [7,8].…”