Share Email Print

Proceedings Paper

Clustering methods for multiresolution simulation modeling
Author(s): Christos G. Cassandras; Christakis G. Panayiotou; Gregory Diehl; Weibo Gong; Zheng Liu; Changchun Zou
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top