A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process
| Autor: | Jianxu Luo, Junxiang Cao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Engineering
TLBO algorithm business.industry Process (computing) Stability (learning theory) Failure data fault diagnosis Fault (power engineering) Machine learning computer.software_genre Class (biology) Swarm intelligence Support vector machine lcsh:TA1-2040 TE chemical process support vector machine Artificial intelligence business Teaching learning lcsh:Engineering (General). Civil engineering (General) computer |
| Zdroj: | MATEC Web of Conferences, Vol 22, p 05016 (2015) |
| Popis: | Teaching-Learning-Based Optimization (TLBO) is a new swarm intelligence optimization algo- rithm that simulates the class learning process. According to such problems of the traditional TLBO as low opti- mizing 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 individu- als 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 com- bining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain im- provement in the accuracy of fault diagnosis and classification. |
| Databáze: | OpenAIRE |
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