Department of Industrial Engineering, Iran University of Science and Technology, Tehran, P.O. Box 16846-13114, Iran.
Abstract
When a process shifts to an out-of-control condition, a search should be initiated to identify and eliminate the special cause(s) manifested to the technical speci cation(s) of the process. In the case of a process (or a product) involving several correlated technical speci cations, analyzing the joint e ects of the correlated speci cations is more complicated compared to a process involving only one technical speci cation. Most real cases refer to processes involving more than one variable. The complexity of a solution to monitor the condition of these processes, estimate the change point and identify further knowledge leading to root-cause analysis motivated researchers to develop solutions based on Arti cial Neural Networks (ANN). This paper provides, analytically, a comprehensive literature review on monitoring multivariate processes approaching arti cial neural networks. Analysis of the strength and weakness of the proposed schemes, along with comparing their capabilities and properties,, are also considered. Some opportunities for new researches into monitoring multivariate environments are provided in this paper
Atashgar, K. (2015). Monitoring multivariate environments using arti cial neural network approach: An overview. Scientia Iranica, 22(6), 2527-2547.
MLA
K. Atashgar. "Monitoring multivariate environments using arti cial neural network approach: An overview". Scientia Iranica, 22, 6, 2015, 2527-2547.
HARVARD
Atashgar, K. (2015). 'Monitoring multivariate environments using arti cial neural network approach: An overview', Scientia Iranica, 22(6), pp. 2527-2547.
VANCOUVER
Atashgar, K. Monitoring multivariate environments using arti cial neural network approach: An overview. Scientia Iranica, 2015; 22(6): 2527-2547.
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