Control chart pattern classification using fourier descriptors and neural networks

Abstract

This paper presents the method of Fourier descriptors and neural networks developed for control chart pattern analysis. The pattern analysis is important to achieve appropriate control and to produce high quality products. This paper also investigates the use of features extracted from Fourier descriptors as the Fourier coefficient components. The Fourier coefficients used to train the neural networks for classifying patterns. Thus, the networks were able to identify the classes. This research concluded the extracted features to improve the performance of the number of Fourier coefficients for neural network training. Experimental results and comparisons based on simulated and unknown data show that the proposed approach performs better than the symbol-sequence histogram with neural network approach.

A Hybrid Expert Decision Support System Based on Artificial Neural Networks in Process Control of Plaster Production – An Industry 4.0 Perspective

“…[24] using the method of Fourier descriptors and NNs identifies the CCPs. In the [25] the hybrid model of Recurrent Neural Network (RNN) and regression were utilized to recognize the CCPs. [26] developed a NN classifier for CCPs by Generalized Autoregressive Conditional Heteroskedasticity (GARH) Model.…”