Car assembly line fault diagnosis based on modified support vector classifier machine

Authors:
Qi Wu
Affiliations:
Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing 211189, China and Key Laboratory of Measurement and Control of CSE (School of ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 17
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Abstract

It is difficult to obtain accurately the solution to parameter b in the final decision-making function of support vector classifier (SVC) machine. By a proposed transformation, parameter b is considered into confidence interval of @n-SVC model. Then this paper proposes a new @n-support vector classifier machine (N@n-SVC). To seek the optimal parameter of N@n-SVC, particle swarm optimization (PSO) is proposed. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on N@n-SVC and PSO is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves this method is equivalent to standard @n-SVC.