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. In the real world, there exist many problems people have not had a fundamental understanding of, and the information people are obtaining is uncertain information. Most human knowledge, however , is typically expressed in vague and imprecisely defined concepts and the inference is mostly supported by common-sense and intuitive reasoning (Ribaric & Hrkac, 2012). Aiming to problem solving in specific areas, the knowledge-based systems depends not only on theoretical knowledge determined in specific areas, but more on experience and common sense of experts. The uncertainty of objective things or phenomena in the real world leads to the fact that people's information and knowledge in various cognitive domains are mostly inaccurate, which requires that the knowledge representation and processing model in the expert system can reflect this uncertainty. Therefore, how to represent and process the uncertainty of knowledge has become one of the important research issues on artificial intelligence. There are important issues underlying knowledge representation that have not yet been adequately addressed. Such issues are those of the modeling and verification of the knowledge-based systems (Ashon, 1995; Mengshoel & Delab, 1993). As a case in point, the conventional techniques such as simulation methods and analytical methods do not provide tools for representing the dynamic behavior of the KBSs as well as for modeling the different aspects of fuzzy information of these systems As an important modeling and computational paradigm, Petri nets (PN) have been widely used (Murata, 1998). Machine learning with fuzzy AND-OR neurons and with fuzzy Petri nets have been proposed by Pedrycz (1989). In order to deal with uncertain information or knowledge, fuzzy Petri nets (FPNs) have been introduced , which can be used to represent Horn clauses or Non-Horn clauses and represent and execute the fuzzy rules To …