NNs Recognize Chaotic Attractors

Author

Teodorescu, Horia-Nicolai L. ; Hulea, Mircea Gh

Author_Institution

Dept. Electron., Telecommun. & Inf. Technol., Tech. Univ. Gheorghe Asachi of Iasi, Iasi, Romania

fYear

2013

fDate

29-31 May 2013

Firstpage

52

Lastpage

57

Abstract

We demonstrate that visual (geometric) patterns can be robustly recognized by an artificial retina composed of a chaotic sensitive system where the coding of the patterns is by attractor features and an artificial neural network is used to classify the attractors. This opens the door to sensorial systems that mimic the biological ones. The specificity of solutions of chaotic systems to their parameters and the universal approximation capability of ANNs form the theoretical foundations of this research. This paper is a preliminary publication.

Keywords

approximation theory; chaos; electronic engineering computing; neural nets; pattern recognition; ANN approximation capability; artificial neural network; attractor feature; biological system; chaotic attractor recognition; chaotic sensitive system; pattern coding; sensorial system; visual pattern; Artificial neural networks; Biological neural networks; Chaos; Neurons; Pattern recognition; Training; Visualization; chaotic circuit; multilayer perceptron; visual pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Systems and Computer Science (CSCS), 2013 19th International Conference on

Conference_Location

Bucharest

Print_ISBN

978-1-4673-6140-8

Type

conf

DOI

10.1109/CSCS.2013.7

Filename

6569243