NDLI: Application of neural networks in production system's simulation

Content Provider	IEEE Xplore Digital Library
Author	Moniaci, W. Carmellino, P. Pasero, E.
Copyright Year	2005
Description	Author affiliation: Ind. Production Syst. Eng. Dept., Turin Polytech., Italy (Moniaci, W.; Carmellino, P.)
Abstract	Nowadays the most powerful tool to evaluate complex production processes' performance is simulation software. This approach is computationally slow. So one alternative could be an approximation of the original system's model able to detect the relationships between input and output, yet it's computationally more efficient than simulation. During training, a neural network is presented with several input/output pairs, and it learns the functional relationship between input and outputs of the simulation model. The network can guess the output for inputs other than the ones presented during training. Usually it's unknown what are the most significant variables for the output of a system. For this problem, it can be used as a data mining preprocess analysis to see the influence of each input parameter on the performance of the production process. In this paper it is shown a statistical nonparametric method, based on 8Parzen window, to make a data mining analysis and then a multilayer perceptron to approximate the original system.
Starting Page	827
Ending Page	831
File Size	291105
Page Count	5
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571249
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Production systems Computational modeling Data mining Data analysis Application software Software performance Software tools Power system modeling Data preprocessing neural network Metamodel cross-entropy approximation
Content Type	Text
Resource Type	Article

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