Title :
Prediction for Process Capability Index Based on Bayesian Framework LS-SVM
Author_Institution :
Dept. of Econ. & Manage., Fujian Univ. of Technol., Fuzhou, China
Abstract :
A method of forecasting process capability index was recommended based on least squares support vector machines (LS-SVM). The parameters of LS-SVM were optimized by Bayesian framework. The higher precision model of prediction for process capability index was built by optimizing parameters. The prediction results show it have many advantage, such as lower error and higher fitting, and it can be used to prediction for process capability index.
Keywords :
Bayes methods; forecasting theory; least squares approximations; manufacturing industries; process capability analysis; support vector machines; Bayesian framework LS-SVM; forecasting; least squares support vector machines; process capability index; Artificial neural networks; Bayesian methods; Computational modeling; Indexes; Kernel; Predictive models; Support vector machines;
Conference_Titel :
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5391-7
Electronic_ISBN :
978-1-4244-5392-4
DOI :
10.1109/CISE.2010.5677266
