In this paper we present an algorithm that allows to select the input variables of Boolean networks from incomplete data. More precisely, sets of input variables, instead of single variables, are evaluated using mutual information to find the combination that maximizes the mutual information of input and output variables. To account for the incompleteness of the data bootstrap aggregation is us...

Input variable selection is a key preprocess step in any I/O modelling problem. Normally, better generalization performance is obtained when unneeded parameters coming from irrelevant or redundant variables are eliminated. Information theory provides a robust theoretical framework for performing input variable selection thanks to the concept of mutual information. Nevertheless, for continuous v...

This paper discusses a new approach to representing a finite automaton as a combination of a linear state equation with a smaller set of free binary variables (i.e., input variables) and binary inequalities, in order to reduce the computational time for solving the model predictive control problem of a class of hybrid systems. In particular, this paper is devoted to proving that a system repres...