Improving the quality of supervised finite-state machine construction using real-valued variables

The use of finite-state machines (FSMs) is a reliable choice for control system design since they can be formally verified. In this paper a problem of constructing FSMs with real-valued input and control parameters is considered. It is supposed that a set of human-created behavior examples, or tests, is available. One of the earlier approaches for solving the problem suggested using genetic algorithms together with a transition labeling algorithm. This paper improves this approach via the use of real-valued variables which are calculated using the FSM's input data. FSMs with real-valued variables are represented as systems of linear controllers. The new approach allows to synthesize FSMs of better quality than it was possible earlier.