A Comparative Approach between Deep Learning and MLE for Monitoring Multivariate Processes with Chaotic Trends

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Rahimi Movassagh, Maryam | 2018

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 51407 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Akhavan Niaki, Taghi
Abstract:
There are a variety of multivariate industrial processes in real world problems. It seems to be necessary to control them through strong tools such as control charts. One of the state-of-the-art methods to monitor processes is neural network. Neural networks are data processing systems inspired by human brain and they are capable of processing data with a variety of small processors working in parallel forming an integrated network to solve a problem. Chaotic models, one of the states of being out of control, are deterministic non-linear models which have extremely complex behavior under determined assumption. Researches have shown neural networks have excellent performance in such systems. In this research, a neural network has been made to estimate the anomalies when there exists chaos. The proposed model will be used in a real case to determine type of the data. This scenario will be used to separate slowing from seizure existing in encephalographic signals where chaos occurs due to simultaneous activity of millions of neurons
Keywords:
Neural Networks ; Statistical Inference ; Quality Control ; Nonlinear Model ; Autoencoder ; Stacked Autoencoders

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