Dynamic representation of fuzzy knowledge based on fuzzy petri net and genetic-particle swarm optimization

Information in some fields like complex product design is usually imprecise, vague and fuzzy. Therefore, it would be very useful to design knowledge representation model capable to be adjusted according to information dynamics. Aiming at this objective, a knowledge representation scheme is proposed, which is called DRFK (Dynamic Representation of Fuzzy Knowledge). This model has both the features of a fuzzy Petri net and the learning ability of evolutionary algorithms. An efficient Genetic Particle Swarm Optimization (GPSO) learning algorithm is developed to solving fuzzy knowledge representation parameters. Being trained, a DRFK model can be used for dynamic knowledge representation and inference. Finally, an example is included as an illustration.

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