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Petri net models of fuzzy neural networks

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1 Author(s)
Ahson, S.I. ; Dept. of Comput. Eng., King Saud Univ., Riyadh, Saudi Arabia

Artificial neural networks (ANN's) are highly parallel and distributed computational structures that can learn from experience and perform inferences. Petri nets, on the other hand, provide an effective modeling framework for distributed systems. The basic concepts of Petri net are utilized to develop ANN-like multilayered Petri net architectures of distributed intelligence having learning ability. A Petri net model of single neuron is presented. A two-layer Petri net model-neural Petri net (NPN)-that uses this neuron model as a building block is described. A new class of Petri nets called the fuzzy neural Petri net (FNPN) is defined. The FNPN can be used for representing a fuzzy knowledge base and for fuzzy reasoning. Some application examples for the two Petri net based models are given

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Systems, Man and Cybernetics, IEEE Transactions on  (Volume:25 ,  Issue: 6 )