23 June 2000 Clustering methods for multiresolution simulation modeling
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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, Christos G. Cassandras, Christakis G. Panayiotou, Christakis G. Panayiotou, Gregory Diehl, Gregory Diehl, Weibo Gong, Weibo Gong, Zheng Liu, Zheng Liu, Changchun Zou, 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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