Title :
Recognition of hidden parameters in quality level measurement
Author :
Gengbao, Huang ; Genbao, Zhang ; Haifeng, Zeng ; Guoqiang, Wang ; Likun, Liu
Abstract :
Because of the existence of hidden parameters for the quality, the output is always artificially in good result during the quality measurement process, and the computer can not show the advantages of learning ability. According to the objectivity of quality factors and different evaluation groups, a method of three-dimensional quality measurement indicator having vector significance is presented. In view of the different space of hidden parameters, different learning methods and ways of calculation are set, and the parallel learning machine scheme consisting of a compound linear matrix and support vector machine is formed, which indirectly isolates protective channel of hidden parameters.
Keywords :
Q-factor; learning (artificial intelligence); pattern recognition; quality control; support vector machines; compound linear matrix; hidden parameter recognition; learning machine; learning methods; quality factors; quality level measurement process; support vector machine; three-dimensional quality measurement indicator; vector significance; Chromium; Concurrent computing; Learning systems; Level measurement; MATLAB; Machine learning; Mechanical engineering; Protection; Q factor; Support vector machines; Hidden; Quality factors; Support vector machine; Three-dimensional quality measurement; parallel learning machine;
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
DOI :
10.1109/CHICC.2008.4605764
