An evolution method of driving seat comfort based on least squares support vector regression

  • Authors:

  • Zhi-Qiang Zeng;Qun Wu;Cheng Yang;Ke-Shou Wu

  • Affiliations:

  • Department of Computer Science and Engineering, Xiamen University of Technology, Xiamen, China and College of Computer Science and Engineering, Zhejiang University, Hangzhou, China;College of Computer Science and Engineering, Zhejiang University, Hangzhou, China;Zhejiang University City College, Hangzhou, China;Department of Computer Science and Engineering, Xiamen University of Technology, Xiamen, China

  • Venue:
  • ICNC'09 Proceedings of the 5th international conference on Natural computation
  • Year:
  • 2009

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Abstract

An evaluation method based on support vector regression (SVR) is put forward for the purpose of predicting subjective perceptions of automobile seat comfort. The inputs included fourteen seat interface pressure measures, three anthropometric. The output was an overall comfort index derived from occupant responses to a survey. In process of experimental data analysis, the algorithm of the least squares support vector regression (LSSVR) was used. The experimental results show that support vector regression model in a number of superior performance on the widely-used artificial neural network prediction model, results of this study will help automotive manufacturers improve car seat in the comfort of the process to reduce costs and shorten the manufacturing time for the car seat provides the industrial design aspects of the manmachine engineering evaluation method.