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Proceedings Paper

Clustering methods for multiresolution simulation modeling
Author(s): Christos G. Cassandras; Christakis G. Panayiotou; Gregory Diehl; Weibo Gong; Zheng Liu; Changchun Zou
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Paper Abstract

Simulation modeling of complex systems is receiving increasing research attention over the past years. In this paper, we discuss the basic concepts involved in multi- resolution simulation modeling of complex stochastic systems. We argue that, in many cases, using the average over all available high-resolution simulation results as the input to subsequent low-resolution modules is inappropriate and may lead to erroneous final results. Instead high- resolution output data should be classified into groups that match underlying patterns or features of the system behavior before sensing group averages to the low-resolution modules. We propose high-dimensional data clustering as a key interfacing component between simulation modules with different resolutions and use unsupervised learning schemes to recover the patterns for the high-resolution simulation results. We give some examples to demonstrate our proposed scheme.

Paper Details

Date Published: 23 June 2000
PDF: 12 pages
Proc. SPIE 4026, Enabling Technology for Simulation Science IV, (23 June 2000); doi: 10.1117/12.389385
Show Author Affiliations
Christos G. Cassandras, Boston Univ. (United States)
Christakis G. Panayiotou, Boston Univ. (United States)
Gregory Diehl, Network Dynamics, Inc. (United States)
Weibo Gong, Univ. of Massachusetts/Amherst (United States)
Zheng Liu, Univ. of Massachusetts/Amherst (United States)
Changchun Zou, Univ. of Massachusetts/Amherst (United States)


Published in SPIE Proceedings Vol. 4026:
Enabling Technology for Simulation Science IV
Alex F. Sisti, Editor(s)

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