23 June 2000 Clustering methods for multiresolution simulation modeling
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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.
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Christos G. Cassandras, Christakis G. Panayiotou, Gregory Diehl, Weibo Gong, Zheng Liu, Changchun Zou, "Clustering methods for multiresolution simulation modeling", Proc. SPIE 4026, Enabling Technology for Simulation Science IV, (23 June 2000); doi: 10.1117/12.389385; https://doi.org/10.1117/12.389385
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