计算机与数字工程2019，Vol.47Issue(6)：1331-1337,7.DOI:10.3969/j.issn.1672-9722.2019.06.010
基于改进LSSVM的短波收信天线智能诊断研究
Intelligent Diagnosis Research of Short-wave Receiving Antenna Based on Improved LSSVM
摘要
Abstract
For a long time,the short wave receiving antenna system has been lack of intelligent automatic monitoring technolo?gy and means,and it is difficult to realize real-time evaluation and early warning of the receiving effect of the equipment. In order to reduce the burden of the basic communication security personnel and provide reliable data support and technical support for the analysis and evaluation of the health condition of short wave receiving antennas and the maintenance and guarantee of the equip?ment,in this paper,a least squares support vector machine classifier based on fruit fly optimization algorithm is established,and the intelligent diagnosis of short wave receiving antenna is realized. To solve the sparseness of the least squares support vector ma?chines,considering the two aspects of the representative samples and the boundary samples,a LSSVM training algorithm based on KFCM clustering algorithm is proposed. The UCI experimental results show that this method takes advantages of KFCM clustering to extract samples with more abundant heuristic information and remove redundant information effectively,a classifier with better per?formance is achieved.关键词
智能诊断/核模糊C均值聚类/最小二乘支持向量机/果蝇优化算法Key words
intelligent diagnosis/kernel fuzzy C-means clustering/least square support vector machine/fruit fly optimiza⁃tion algorithm分类
信息技术与安全科学引用本文复制引用
向奕雪,陈斌,罗勇..基于改进LSSVM的短波收信天线智能诊断研究[J].计算机与数字工程,2019,47(6):1331-1337,7.
