Petri net models of fuzzy neural networks

Author

Ahson, Syed I.

Author_Institution

Dept. of Comput. Eng., King Saud Univ., Riyadh, Saudi Arabia

Volume

25

Issue

6

fYear

1995

fDate

6/1/1995 12:00:00 AM

Firstpage

926

Lastpage

932

Abstract

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

Keywords

Petri nets; fuzzy logic; fuzzy neural nets; inference mechanisms; uncertainty handling; Petri net models; distributed intelligence; fuzzy knowledge base; fuzzy neural Petri net; fuzzy neural networks; fuzzy reasoning; multilayered Petri net architectures; synaptic weight; Artificial neural networks; Computer architecture; Computer networks; Concurrent computing; Distributed computing; Fuzzy neural networks; Fuzzy reasoning; High performance computing; Neurons; Petri nets;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.384255

Filename

384255