NDLI: Iterative RBF neural networks as metamodels of stochastic simulations

Content Provider	IEEE Xplore Digital Library
Author	Meghabghab, G. Nasr, G.
Copyright Year	1999
Description	Author affiliation: Dept. of Math. & Comput. Sci., Valdosta State Univ., GA, USA (Meghabghab, G.)
Abstract	Research into emerging technological approaches to make computer simulations more effective and efficient is an essential ingredient to developing successful manufacturing models. This study is a premiere study in using neural networks in metamodeling stochastic simulation in manufacturing domain. A new iterative RBF neural network was developed rather than the baseline ANN models which were used in stochastic simulation metamodeling in domains such as combat simulations in the military, service industries, and transportation companies. Given the fact that typical stochastic simulation metamodeling approaches involves the use of regression models in response surface methods, RBF become a natural target for such an attempt because they use a family of surfaces each of which naturally divides an input space into 2 regions and the n patterns will be assigned either class X+ or X-. This dichotomy of the points is said to be separable with respect to the family of surfaces if there exists a surface in the family that separates the points in the class X+ from those in the class X-. In fact, for the evaluation of the quality of a ball steel, RBF metamodel trained on 1521 training examples from a set of 13000 different simulation runs and was able to outperform direct simulation on 120 additional test examples which were not included in the training set.
Starting Page	729
Ending Page	734
File Size	771269
Page Count	6
File Format	PDF
ISBN	0780354893
DOI	10.1109/IPMM.1999.791478
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Defense industry Computer simulation Computer aided manufacturing Computational modeling Neural networks Stochastic processes Metamodeling Artificial neural networks Response surface methodology Virtual manufacturing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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