• 论文
主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于PSO优化NP-FSVM的煤矸光电智能分选技术研究
  • Title

    Research on photoelectric intelligent separation technology of coal and gangue based on NP-FSVM with the PSO algorithm

  • 作者

    郭永存于中山卢熠昌

  • Author

    GUO Yongcun,YU Zhongshan, LU Yichang

  • 单位

    省部共建深部煤矿采动响应与灾害防控国家重点实验室安徽理工大学安徽理工大学机械工程学院

  • Organization
    1.Anhui University of Science and Technology,State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines,Huainan ,China;2.School of Mechanical Engineering,Anhui University of Science and Technology,Huainan ,China
  • 摘要

    为提高分选的稳定性和准确率,提出一种多特征融合的基于粒子群算法优化的法平面型隶属度函数模糊支持向量机(PSO-NP-FSVM)煤矸石分选方法。介绍了X射线探测识别煤矸石技术的基本原理与工作流程。对采集到的X射线图像经中值滤波去噪预处理后,分别提取灰度特征下的灰度均值、灰度方差,以及基于灰度共生矩阵的纹理特征下的能量、相关性、对比度和熵共计6个特征向量,并对选择的特征进行融合。利用法平面型隶属度函数能有效剔除孤立样本的优点,结合粒子群算法对模糊支持向量机分类器模型的主要参数进行优化,提出经优化改进后的PSO-NP-FSVM分类算法,采用相同的训练样本,与PSO-FSVM分类器模型进行仿真对比分析。最后,分别采用PSO-NP-FSVM、PSO-FSVM算法与单一灰度或纹理特征进行识别的方法建立分类器模型,并通过交叉验证的方法进行对比试验。试验研究结果表明:PSO-NP-FSVM算法经56次的迭代,参数达到最优,PSO-FSVM算法参数寻优需迭代63次;PSO-NP-FSVM算法的适应度函数值较小。通过多特征融合的PSO对NP-FSVM进行优化的分选方法,煤矸石的分选准确率达到93.8 %,其准确率和稳定性较普通PSO-FSVM分类器模型与单一特征识别方法,均有所提高。X射线探测的光电智能分选技术是未来煤矸石分选发展的重要趋势,此方法可改善在分选过程中因煤矸厚度的影响,导致识别准确率降低的缺陷。


  • Abstract
    In order to improve the stability and accuracy of coal and gangue separation, a multi-feature fusion method based on Fuzzy Support Vector Machine with Normal Plane membership function for Particle Swarm Optimization algorithm (PSO-NP-FSVM) is proposed. The basic principle and workflow of coal and gangue recognition by X-ray detection technology are introduced.After the collected X-ray image pre-processing by median filtering, the gray mean, gray variance under grayscale characteristic and the energy, correlation, contrast, and entropy under the texture characteristic based on the gray level co-occurrence matrix of a total of six characteristic quantities of coal and gangue are extracted respectively. And the selected characteristics is fused. The merits of isolated samples can be effectively eliminated by using the Normal Plane membership function, and the main parameters of the Fuzzy Support Vector Machine classifier model are optimized by combining the Particle Swarm Optimization algorithm. An improved PSO-NP-FSVM classification algorithm is proposed.Using the same training samples, the simulation results are compared with PSO-FSVM classifier model. Finally, the PSO-NP-FSVM, PSO-FSVM and single grayscale characteristic or texture characteristic are used to establish the classifier model, and the cross-validation method is used to carry out comparative experiments. The experimental results show that the PSO-NP-FSVM algorithm achieves the optimal parameters after 56 iterations, and the PSO-FSVM algorithm needs to be iterated 63 times. The PSO-NP-FSVM algorithm has a small fitness function value. And the classification accuracy of coal and gangue is 93.8% through the multi-feature PSO-NP-FSVM method. The accuracy and stability of the new classifier model and single characteristic recognition are better than the common PSO-FSVM classifier model. The photoelectric intelligent separation technology of X-ray detection is an important trend in the future development of coal and gangue sorting. This method can improve the defect of recognition accuracy caused by the influence of thickness for coal and gangue during the sorting process.
  • 关键词

    煤矸石分选智能分选X射线探测灰度特征纹理特征粒子群优化算法模糊支持向量机

  • KeyWords

    separation of coal and gangue; intelligent separation;X-ray detection; grayscale characteristic; texture characteristic; particle swarm optimization algorithm; fuzzy support vector machine

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主办单位:煤炭科学研究总院有限公司 中国煤炭学会学术期刊工作委员会

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