一种改进的边缘层节点健康态势预估方法

doi:10.19665/j.issn1001-2400.2020.03.005

Abstract

Abstract:

An improved state prediction algorithm for edge layer nodes is proposed to solve the problem of the existing state prediction algorithm for edge layer nodes based on Hidden Markov, such as the subjectivity of initial parameter selection, the dependence of feature weights setting on experience, and the bad adaptability of multidimension feature node analysis. At the data processing layer of the algorithm, the parameter of the model and observation sequence are optimized by the method of clustering; and then at the training layer of the algorithm, the single-feature Hidden Markov Model is used to model the multi-feature Hidden Markov Model; finally, an adaptive genetic algorithm based on the information gain is used to optimize and reduce the state sequence generated by the Hidden Markov Model. The problems of feature weight setting and parameter initial value selection are solved effectively. Experimental results show that the proposed algorithm effectively improves the accuracy of the high-dimensional health state of large-scale edge layer nodes compared with the existing algorithms.

Key words: edge layer nodes, k-means, hidden Markov model, adaptive genetic algorithm

CLC Number:

TP311

SUN Qian,ZHANG Jiarui,GAO Ling,WANG Yuxiang,YANG Jianfeng. Health prediction algorithm for edge layer nodes[J].Journal of Xidian University, 2020, 47(3): 32-39.

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References 21

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Health prediction algorithm for edge layer nodes

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