Performance Analysis of ANN on Dataset Allocations for Pattern Recognition of Bivariate Process

Abstract

Several approaches to identifying the out-of-control variables after the detection of abnormal pattern has been most intensively studied and used in practice. One of the several approaches is the Artificial Neural Network (ANN) based model for diagnosis of out-of-control signal of multivariate process mean shift. In spite of the number of years of research in neural network, limited research (if any) have been done on the effect of dataset allocations in percentages for training and testing on the performance of ANN. In this paper, we investigate the use of different percentages of dataset allocation into training, validation and testing on the performance of ANN in pattern recognition of bivariate process using six selected training algorithms. The result of study showed that large allocation of dataset for training was found suitable, having higher recognition accuracy for ANN learning and perform better for pattern recognition of bivariate process.

Keywords: Bivariate Process; Pattern Recognition; Recognition accuracy; Multivariate quality control charts, training algorithm