Investigation of System Identification Techniques for Simulation Model Abstraction

摘要

This report summarizes research into the application of system identification techniques to simulation model abstraction. System identification produces simplified mathematical models that approximate the dynamic behaviors of the underlying stochastic simulations. Four state-space system identification techniques were examined: Canonical State-space, Compartmental Models, Maximum Entropy, and Hidden Markov Models (HMM). Two stochastic simulation models were identified: the 'Attrition Simulation', a simulation of two opposing forces, each operating with multiple weapon system types; and the 'Mission Simulation', a simulation of a squadron of aircraft performing battlefield air interdiction. The system identification techniques were evaluated and compared under a variety of scenarios on how well they replicate the distributions of the simulation states and decision outputs. Encouraging results were achieved by the HMM technique applied to Attrition Simulation - and by the Maximum Entropy technique applied to the Mission Simulation. This report also discusses the run-time performance of the algorithms, the development of suitable model structures, and implications for future efforts.