An IPSO-SVM algorithm for security state prediction of mine production logistics system

4th International Conference on Manufacturing and Industrial Technologies

机械制造;工业技术

Zhang, Yanliang^1 ; Lei, Junhui^1 ; Ma, Qiuli^1 ; Chen, Xin^1 ; Bi, Runfang^1

School of Management Engineering, Zhengzhou University, Zhengzhou

450001, China^1

关键词: Classification standard;    Convergence speed;    Mine production;    Parameter setting;    Predicting models;    Search accuracy;    Security warning;    State prediction;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/212/1/012016/pdf
DOI  :  10.1088/1757-899X/212/1/012016

学科分类：工业工程学

来源: IOP

【 摘 要 】

A theoretical basis for the regulation of corporate security warning and resources was provided in order to reveal the laws behind the security state in mine production logistics. Considering complex mine production logistics system and the variable is difficult to acquire, a superior security status predicting model of mine production logistics system based on the improved particle swarm optimization and support vector machine (IPSO-SVM) is proposed in this paper. Firstly, through the linear adjustments of inertia weight and learning weights, the convergence speed and search accuracy are enhanced with the aim to deal with situations associated with the changeable complexity and the data acquisition difficulty. The improved particle swarm optimization (IPSO) is then introduced to resolve the problem of parameter settings in traditional support vector machines (SVM). At the same time, security status index system is built to determine the classification standards of safety status. The feasibility and effectiveness of this method is finally verified using the experimental results.