Fuzzy and chaotic neuro-network modeling

Author

Garliauskas, Algis

Author_Institution

Lab. of Neuroinformatics, Inst. of Math. & Informatics, Vilnius, Lithuania

Volume

1

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

428

Lastpage

431

Abstract

The origin of fuzzy signals lies in the complex biochemical and electrical processes of the synapse and dendrite membrane excitation and in the inhibition mechanism. The mathematical operations included in fuzzy neural network modeling are described. The scalar product between the inputs of layers and synaptic weights is replaced by a fuzzy logic multiplication. The sum of products changes into fuzzy logic sums, and operators such as supremum, maximum and minimum are presented for fuzzy description. Both fuzzy properties and a chaos phenomenon are analyzed based on experimental computations

Keywords

chaos; fuzzy neural nets; mathematical operators; modelling; biochemical processes; chaotic neuro-network modeling; dendrite membrane excitation; electrical processes; fuzzy backpropagation; fuzzy description; fuzzy logic multiplication; fuzzy logic sums; fuzzy membership; fuzzy neural network modeling; fuzzy signals; inhibition mechanism; mathematical operations; maximum operator; minimum operator; scalar product; supremum operator; synapse excitation; synaptic weights; Artificial neural networks; Biological neural networks; Biomembranes; Chaos; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Neural networks; Neurons; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2001. The 10th IEEE International Conference on

Conference_Location

Melbourne, Vic.

Print_ISBN

0-7803-7293-X

Type

conf

DOI

10.1109/FUZZ.2001.1007340

Filename

1007340