文献类型：会议论文

英文题名：A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

作者：Cao, Junxiang[1];Luo, Jianxu[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

会议论文集：International Conference on Engineering Technology and Application (ICETA)

会议日期：MAY 29-30, 2015

会议地点：Xiamen, PEOPLES R CHINA

语种：英文

外文关键词：TLBO algorithm; support vector machine; fault diagnosis; TE chemical process

摘要：Teaching-Learning-Based Optimization (TLBO) is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.