Implementación SVM y RNA para Reconocimiento de Patrones Multivariantes mediante Dispersión de Datos. (SVM and ANN Application to Multivariate Pattern Recognition Using Scatter Data)

Pamela Chiñas (pame.ch.s@gmail.com)1, Ismael Lopez (ismael.lopez@cinvestav.edu.mx)1, José Antonio Vazquez (antonio.vazquez@itcelaya.edu.mx)2, Roman Osorio (roman@servidor.unam.mx)3, Gaston Lefranc (glefranc@ucv.cl)4


1Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional
2Instituto Tecnológico de Celaya
3Universidad Nacional Autónoma de México
4Pontificia Universidad Católica de Valparaíso

This paper appears in: Revista IEEE América Latina

Publication Date: May 2015
Volume: 13,   Issue: 5 
ISSN: 1548-0992


Abstract:
several methods of Statistical Process Control (SPC) are used to analyze process measurements with the purpose to detect faults that affect the process stability. SPC has a major drawback because it indicates the presence of faults without explaining which ones and where are the faults. In practical applications, SPC just analyses univariate signals limiting the study of multiple measures. Nowadays, novel methods have been developed for fault analysis based on pattern recognition in control charts. However, the majority of these studies follow a univariate approach. This article proposes a multivariate pattern recognition approach using machine learning algorithms in conjunction with a scatter diagram as the proposed method. In particular the aim of this approach is to monitor quality characteristics of a product in a multivariate environment considering states in control and out of control without the constraints of statistical conditions with the possibility of its application in real time.Results using Support Vector Machines (SVM) and the FuzzyARTMAP neural network showed that multivariate patterns can be recognized successfully in 81% of the cases.

Index Terms:
Multivariate patterns, SVM, Fuzzy ARTMAP, PCA, univariate patterns.   


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