An approach to estimating product design time based on fuzzy v-support vector machine.
This paper presents a new version of fuzzy support vector machine (FSVM) developed for product design time estimation. As there exist problems of finite samples and uncertain data in the estimation, the input and output variables are described as fuzzy numbers, with the metric on fuzzy number space defined. Then, the fuzzy v-support vector machine (Fv-SVM) is proposed on the basis of combining the fuzzy theory with the v-support vector machine, followed by the presentation of a time estimation method based on Fv-SVM and its relevant parameter-choosing algorithm. The results from the applications in injection mold design and software product design confirm the feasibility and validity of the estimation method. Compared with the fuzzy neural network (FNN) model, our Fv-SVM method requires fewer samples and enjoys higher estimating precision.
Yan
Hong-Sen
HS
Research Institute of Automation, Southeast University, Nanjing 210096, China. hsyan@seu.edu.cn
Xu
Duo
D
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017076:Computer-Aided Design; D003661:Decision Support Techniques; D004867:Equipment Design / Q000379:methods*; D017143:Fuzzy Logic; D016247:Information Storage and Retrieval / Q000379:methods; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors