caa a22 4500 605469482 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1293-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1293-x A probabilistic artificial neural network-based procedure for variance change point estimation [Elektronische Daten] [Amirhossein Amiri, S. Niaki, Alireza Moghadam] Control charts are useful tools of monitoring quality characteristics. One of the problems of employing a control chart is that the time it alarms is not synchronic with the time when assignable cause manifests itself in the process. This makes difficult to search and find assignable causes. Knowing the real time of manifestation of assignable cause (change point) helps to find assignable cause(s) sooner and eases corrective actions to be taken. In this paper, a probabilistic neural network (PNN)-based procedure was developed to estimate the variance change point of a normally distributed quality characteristic. The PNN was selected based on trial and error among different types of artificial neural networks and on the basis of its advantages such as fast training process, converging to optimal classifier and adding or removing samples without extensive retraining. In the proposed procedure, the signal is first received by an $$S^{2}$$ S 2 control chart and then based on the designed tests of hypothesis, which distinguish the size of shift in the variance, a suitable PNN is activated. The performance of the proposed procedure is evaluated through extensive simulation studies. In addition, the results of a comparison study with the maximum likelihood estimation (MLE) method show that the proposed procedure outperforms MLE in estimating the real time of the step change in variance of a normal quality characteristic. Finally, an illustrative example is presented to clarify the procedure step by step. Springer-Verlag Berlin Heidelberg, 2014 Change point nationallicence Variance change point nationallicence Probabilistic artificial neural network nationallicence Statistical process control nationallicence Amiri Amirhossein Department of Industrial Engineering, Shahed University, Tehran, Iran aut Niaki S. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran aut Moghadam Alireza Department of Industrial Engineering, Shahed University, Tehran, Iran aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 691-700 1432-7643 19:3<691 2015 19 500 https://doi.org/10.1007/s00500-014-1293-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1293-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Amiri Amirhossein Department of Industrial Engineering, Shahed University, Tehran, Iran aut NATIONALLICENCE 700 1- Niaki S. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran aut NATIONALLICENCE 700 1- Moghadam Alireza Department of Industrial Engineering, Shahed University, Tehran, Iran aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 691-700 1432-7643 19:3<691 2015 19 500