  We propose an automata theoretic learning algorithm for the identification of
black-box switched linear systems whose switching logics are event-driven. A
switched system is expressed by a deterministic finite automaton (FA) whose
node labels are the subsystem matrices. With information about the dimensions
of the matrices and the set of events, and with access to two oracles, that can
simulate the system on a given input, and provide counter-examples when given
an incorrect hypothesis automaton, we provide an algorithm that outputs the
unknown FA. Our algorithm first uses the oracle to obtain the node labels of
the system run on a given input sequence of events, and then extends Angluin's
\(L^*\)-algorithm to determine the FA that accepts the language of the given
FA. We demonstrate the performance of our learning algorithm on a set of
benchmark examples.
