Petri net models of fuzzy neural networks

Petri net models of fuzzy neural networks

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on systems, man, and cybernetics man, and cybernetics, 1995-06, Vol.25 (6), p.926-932
1. Verfasser: Ahson, S.I.
Format: Artikel
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Artificial neural networks
Computer architecture
Computer networks
Computer science
control theory
systems
Concurrent computing
Connectionism. Neural networks
Distributed computing
Exact sciences and technology
Fuzzy neural networks
Fuzzy reasoning
High performance computing
Neurons
Petri nets
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.< >
ISSN:0018-9472
2168-2909
DOI:10.1109/21.384255