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首页> 外文期刊>ISA Transactions >A robust cutting pattern recognition method for shearer based on Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy
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A robust cutting pattern recognition method for shearer based on Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy

机译：基于混沌修正粒子群优化和在线校正策略的采煤机基于最小二乘支持向量机的鲁棒切割模式识别方法

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摘要

Accurate cutting pattern recognition method for shearer in coal mining process has drawn more and more attention over the past decades due to its important role in guaranteeing the steady operation of the equipment, which, however, remains challenging caused by the mismatch of cutting pattern recognition especially for dynamic uncertainty of future sampled data. Therefore, a novel approach for cutting pattern recognition with an optimal Online Correcting Strategy (OCS) combined with Least Square Support Vector Machine (LSSVM) and Chaos Modified Particle Swarm Optimization (CMPSO) algorithm, named OCS-CMPSO-LSSVM, is proposed, where LSSVM models the functional relationship between input and output of the system, CMPSO optimizes the parameters of LSSVM, and OCS modifies the model to reduce its mismatch as the system runs, respectively. The performance of the proposed model is demonstrated with a simulation experiment and compared with the existing methods reported in the literature in detail. The experimental results reveal that the proposed models can achieve better cutting pattern recognition performance and higher robustness. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
机译：由于在保证设备的稳定运行方面，在过去的几十年里，煤炭采矿过程中煤炭过程中的采煤过程中的准确切割模式识别方法越来越多地引起了越来越多的关注对于未来采样数据的动态不确定性。因此，提出了一种具有最佳在线校正策略（OCS）与最小二乘支持向量机（LSSVM）和混沌修改的粒子群优化（CMPSO）算法的切割模式识别的新方法，名为OCS-CMPSO-LSSVM，其中LSSVM模型系统的输入和输出之间的功能关系，CMPSO优化LSSVM的参数，并且OCS修改模型以减少其不匹配，因为系统运行，减少其不匹配。拟议模型的性能进行了说明了模拟实验，并与文献中的现有方法详细介绍。实验结果表明，所提出的模型可以实现更好的切割模式识别性能和更高的鲁棒性。 （c）2019 ISA。 elsevier有限公司出版。保留所有权利。

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