Simulation with Qualitative Models in Reduced Tensor Representations

Abstract

Abstract The paper proposes simulation algorithms for tensor representations of qualitative models based on stochastic automata. We show that storing the transition probabilities of the automaton in tensor formats will help to break the curse of dimensionality, i.e. to overcome the storage complexity problem of the automaton that occurs due to the exponential growth in the quantity of automata transitions when the number of input, state and output signals of the underlying discrete-time system rises. In addition, we present the application of a modern tensor optimization method for the completion of qualitative models identified by data-driven black-box approaches and thus suffering from the problem of unobserved sets of training data.