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Performance of neural networks for predicting yarn properties using principal component analysis
Author(s) -
Chattopadhyay R.,
Guha Anirban
Publication year - 2003
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.13231
Subject(s) - yarn , principal component analysis , artificial neural network , set (abstract data type) , component (thermodynamics) , process (computing) , computer science , fiber , training set , data set , biological system , artificial intelligence , pattern recognition (psychology) , algorithm , materials science , composite material , physics , biology , thermodynamics , programming language , operating system
In recent years, neural networks have been used as a tool for modeling an industrial process. An improvement in their performance may be expected either by divining more efficient training algorithms or by intelligently manipulating the data set. The second method is examined. The problem chosen is one of predicting the properties of cotton yarn from the fiber properties. When the input data are known to correlate with each other, principal component analysis can be used to improve the performance of neural networks. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 1746–1751, 2004