Identification of stochastic max-plus-linear systems

We present a method to identify the parameters of a state space model for a max-plus-linear discrete event system from measured data. Previous papers report on results with noise-free measured data. In this paper we extend this to identification for perturbed max-plus-linear systems in a stochastic setting. The approach is based on recasting the identification problem as an optimization problem. We show that under quite general conditions the resulting optimization problems can be solved very efficiently using gradient search techniques.