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

• The model of dynamic representation of fuzzy knowledge is proposed.
• The model has both the features of a fuzzy Petri net and the learning ability of evolutionary algorithms.
• The improved Genetic Particle Swarm Optimization (GPSO) learning algorithm can solve fuzzy knowledge representation parameters efficiently.
• The validity of the method has been demonstrated by using it in the fault diagnoses of launch vehicle.

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.