Abstraction of Continuous System to Discrete Event System Using Neural Network

Abstract

A hybrid system consists of continuous systems and discrete event systems, which interact with each other. In such configuration, a continuous system can't directly communicate with a discrete event system. Therefore, a form of interface between two systems is required for possible communication. An interface from a continuous system to a discrete event system requires abstraction of a continuous system as a discrete event system. This paper proposes a methodology for abstraction of a continuous system as a discrete event system using neural network. A continuous system is first represented by a timed state transition model and then the model is mapped into a neural network by learning capability of the network. With a simple example, this paper describes the abstraction process in detail and discusses application methods of the neural network model. Finally, an application of such abstraction in design of intelligent control is discussed.