Self-organization in cellular neural networks: a comparison with Kohonen´s self-organizing maps

Author

Thiran, Patrick

Author_Institution

Fed. Inst. of Technol., Lausanne, Switzerland

fYear

1998

fDate

14-17 Apr 1998

Firstpage

Lastpage

Abstract

The ordinary differential equation (ODE) method is difficult to use for analyzing the self-organization of the Kohonen algorithm. Two stochastic, `self-organizing´ algorithms, whose corresponding ODE is a CNN equation, are presented. Their convergence shares similar features with the Kohonen self-organizing process

Keywords

asymptotic stability; cellular neural nets; convergence; differential equations; probability; self-organising feature maps; stochastic processes; unsupervised learning; Kohonen self-organizing maps; asymptotic stability; cellular neural networks; convergence; differential equation; probability; stochastic process; unsupervised learning; Algorithm design and analysis; Cellular neural networks; Convergence; Differential equations; Intelligent networks; Network topology; Neurons; Self organizing feature maps; Stochastic processes; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and Their Applications Proceedings, 1998 Fifth IEEE International Workshop on

Conference_Location

London

Print_ISBN

0-7803-4867-2

Type

conf

DOI

10.1109/CNNA.1998.685332

Filename

685332

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2625673