Fuzzy-Petri-net reasoning supervisory controller and estimating states of Markov chain models

Conference: IEEE 3rd International Conference on Computational Cybernetics, Victoria, MAURITANIA, APR 13-16, 2005.Markov chain models are efficient tools for representing stochastic discrete event processes with wide applications in decision and control. A novel approach to fuzzy-Petri-net reasoning generated solution to initial or another state in Markov-chain models is proposed. Reasoning is performed by a fuzzy-Petri-net supervisory controller employing a fuzzy-rule production system design and a fuzzy-Petri-net reasoning algorithm, which has been developed and implemented in C++. The reasoning algorithm implements calculation of the degrees of fulfilment for all the rules and their appropriate assignment to places of Petri net representation structure. The reasoning process involves firing active transitions and calculating degrees of fulfilment for the output places, which represent propositions in the knowledge base, and determining of fuzzy-distributions for output variables as well as their defuzzified values. Finally, these values are transferred to assign the state of Markov-chain decision model in terms of transition probabilities.IEEE; Hungarian Fuzzy Assoc; Budapast Tech; Univ Mauritius; IEEE syst, Men, & Cybernet; IEEE Ind Elect Soc; IEEE Reg 8; IEEE Hungary Sect; IEEE Joint Chapter, IES& RAS; IEEE SMC Chapter; IEEE CI Chapter; Eurofuse; Japan Soc Fuzzy Theory & Intelligent Informat; John VonNeumann Comp Soc