Model abstraction for discrete event systems using neural networks and sensitivity information

STRACTION FOR DISCRETE EVENT SYSTEMS USING NEURAL NETWORKS AND SENSITIVITY INFORMATION Christos G. Panayiotou Christos G. Cassandras Department of Manufacturing Engineering Boston University Boston, MA 02215, U.S.A. Wei-Bo Gong Department of Electrical and Computer Engineering University of Massachusetts Amherst, MA 01003, U.S.A. Simulation is one of the most powerful tools for modeling and evaluating the performance of complex systems, however, it is computationally slow. One approach to overcome this limitation is to develop a "metamodel". In other words, generate a "surrogate" model of the original system that accurately captures the relationships between input and output, yet it is computationally more efficient than simulation. Neural networks (NN) are known to be good function approximators and thus make good metamodel candidates. During training, a NN is presented with several input/output pairs, and is expected to learn the functional relationship between i...

• Type: Conference
• Year: 2000
• Where: WSC
• Authors: Christos G. Panayiotou, Christos G. Cassandras, Weibo Gong
• Keywords: Modeling And Simulation, Neural Networks, Sensitivity Information, Simulation, WSC 2000