Hybrid fuzzy support vector classifier machine and modified genetic algorithm for automatic car assembly fault diagnosis

Authors:
Qi Wu
Affiliations:
Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 13
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Abstract

This paper presents a new version of fuzzy support vector machine to diagnose automatic car assembly fault diagnosis, the input and output variables are described as fuzzy numbers and the metric on fuzzy number space is defined. Then by combining the fuzzy theory with v-support vector machine, the fuzzy v-support vector classifier machine (Fv-SVCM) is proposed. A fault diagnosis method based on Fv-SVCM and its relevant parameter-choosing algorithm is put forward. The results of the application in car assembly diagnosis confirm the feasibility and the validity of the diagnosis method. Compared with the fuzzy neural network (FNN) model, Fv-SVCM method requires fewer samples and has better estimating precision.