Learning capability in fuzzy Petri nets

Petri nets (PNs) have been widely used in modeling and analyzing many real applications such as computers, automatic control and management information systems, etc. The power of PNs comes from their ability to model and analyze the behaviors and states of systems (events) concurrently. Neural networks (NNs), on the other hand, were developed to handle and solve many linear and nonlinear complex problems by forming an association (relationship) between its input and output training patterns. It will be advantageous if a learning capability is incorporated into a fuzzy Petri net (FPN) which has the capability of both systems. In this paper, a FPN model which has learning capability is proposed. The purpose of including a learning facility in FPNs is that many parameters of a fuzzy expert system, included in fuzzy production rules (FPRs), once when it has been modeled by a FPN could be tuned. These parameters, including membership values, weights (local and global) and certainty factors etc., play important roles in capturing and representing complex domain expert knowledge. By comparing the artificial neural networks (ANN) with FPNs having learning capability, we have advantages such as: a) FPNs provide a transparent modeling and analyzing capability whereas ANN provides a black-box learning and no-analysis capability; b) FPNs representing a fuzzy expert system could be used to analyze the different inference states step-by-step; c) FPNs could tune parameters in a fuzzy expert system so that the overall system performance is improved.