计算机与现代化Issue(5)：161-163,166,4.DOI:10.3969/j.issn.1006-2475.2012.05.045
基于改进PSO算法和LS-SVM的苹果分级检测
Detection of Apple Grading Based on Improved PSO Algorithm and LS-SVM
摘要
Abstract
To solve the low accuracy and velocity of apple grading, a grading method of LS-SVM based on improved PSO algorithm is presented, which is a new improved form by synthesizing the existing model of PSO. This method can optimize features and reduce the number of training samples greatly before classification, so as to enhance the training efficiency. Experimental results on the Fuji apple database show that the method can extract 5 optimal features from 16 shape features and achieve a high correct identification ratio which is up to 96% , the application effect is very notable so the proposed method is effective.关键词
苹果分级/粒子群优化算法/最小二乘支持向量机Key words
apple grading/ particle swarm optimization (PSO)/ least squares support vector machines (LS-SVM)分类
信息技术与安全科学引用本文复制引用
夏卿,李先锋..基于改进PSO算法和LS-SVM的苹果分级检测[J].计算机与现代化,2012,(5):161-163,166,4.基金项目
盐城工学院重点建设学科开放基金资助项目(XKY2010021) （XKY2010021）
