NDLI: Control chart forecasting: A hybrid model using recurrent neural network, design of experiments and regression

Content Provider	IEEE Xplore Digital Library
Author	Behmanesh, R. Rahimi, I.
Copyright Year	2012
Description	Author affiliation: Group of Mathematic, Vazvan Center, Payame Noor University, Isfahan, Iran (Rahimi, I.) \|\| Dept. Accounting, Khorasgan (Isfahan) Branch, Islamic Azad University, Isfahan, Iran (Behmanesh, R.)
Abstract	Recurrent neural network (RNN) is an efficient tool not only for modeling production control process but also for modeling services. In this paper the combination model of RNN, regression and stepwise regression analysis (SRA) were employed in order to predict the variables of process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of hybrid model. First, the most important factors on forecasting response time as inputs were selected according to SRA. Then, the regression was made for predicting the response time of process based upon obtained inputs, and then the error between actual and predicted response time as output along with input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, design of experiments (DOE) was set so as to optimize the RNN in training process of it.
Starting Page	435
Ending Page	439
File Size	108636
Page Count	5
File Format	PDF
ISBN	9781467304252
e-ISBN	9781467304269
DOI	10.1109/BEIAC.2012.6226098
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-07
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Control chart Stepwise regression Design of Experiments Neurons Process control Control charts Predictive models Regression Recurent Neural Network Time factors Forecasting
Content Type	Text
Resource Type	Article

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