现代电子技术2018，Vol.41Issue(6)：141-144,149,5.DOI:10.16652/j.issn.1004-373x.2018.06.034
基于改进的差异演化算法求解SVM反问题的研究
Research on SVM inverse problem solving based on changed differential evolution algorithm
摘要
Abstract
Since the traditional algorithm has the problems of low efficiency of support vector machine(SVM)inverse prob-lem solving and high algorithm complexity,is easily to fall into local optimum,and is prone to the premature convergence,a new differential evolutionary algorithm based on improved difference is proposed. On the basis of normative differential evolution algorithm,the population classification mechanism is used to improve the algorithm. The experimental design was carried out for the improved algorithm,normative differential evolution algorithm and K-means clustering algorithm. The final experimental re-sults of the algorithm are analyzed. The maximum interval numbers and average interval numbers of the changed differential evo-lution(CDE)algorithm beyond operation time are improved greatly. The algorithm can effectively protect the individual within the optimum solution region but with low adaptive value,improve the local search ability of the algorithm,and is conductive to the realization of global convergence. The experimental results show that the performance of the CDE algorithm is improved obvi-ously for SVM inverse problem solving.关键词
支持向量机/局部最优/差异演化算法/全局收敛/种群分类机制/IRIS数据库Key words
support vector machine/local optimum/differential evolution algorithm/global convergence/population classi-fication mechanism/IRIS database分类
信息技术与安全科学引用本文复制引用
樊永生,熊焰明,余红英..基于改进的差异演化算法求解SVM反问题的研究[J].现代电子技术,2018,41(6):141-144,149,5.基金项目
山西省自然科学基金(201601D102029) Project Supported by Shanxi Provincial Natural Science Foundation(201601D102029) （201601D102029）
