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

作者：Wu, Qi^*

来源：Expert Systems with Applications, 2011, 38(3): 1457-1463.

DOI：10.1016/j.eswa.2010.07.052

• Fuzzy v-support vector classifier machine
• Triangular fuzzy number
• Genetic algorithm
• Fault diagnosis

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.

• 出版日期2011-3
• 单位东南大学