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In this paper, a simulation-based method for the analysis and design ofabstracted models for a stochastic hybrid system is proposed. The accuracy of amodel is evaluated in terms of its capability to reproduce the system outputfor all the realizations of the stochastic input except for a set of (small)probability epsilon (epsilon-abstraction). This naturally leads tochance-constrained optimization problems, which are here tackled by means of arecently developed randomized approach. The main thrust of this paper is that,by testing how close the model and system outputs are over a finite number N ofinput realizations only, conclusions can be drawn about the model capability asan epsilon-abstraction. The key feature of the proposed method is its highversatility since it does not require specific assumptions on the system to beapproximated. The only requirement is that of being able to run multiplesimulations of the system behavior for different input realizations.

Performance assessment and design of abstracted models for stochastic hybrid systems through a randomized approach