A new k-winners-take-all neural network and its array architecture

Author

Yen, Jui-Cheng ; Guo, Jiun-In ; Chen, Hun-Chen

Author_Institution

Dept. of Electron. Eng., Nat. Lien-Ho Coll. of Technol. & Commerce, Taiwan, China

Volume

9

Issue

5

fYear

1998

fDate

9/1/1998 12:00:00 AM

Firstpage

901

Lastpage

912

Abstract

In this paper, a new neural-network model called WINSTRON and its novel array architecture are proposed. Based on a competitive learning algorithm that is originated from the coarse-fine competition, WINSTRON can identify the k larger elements or the k smaller ones in a data set. We will then prove that WINSTRON converges to the correct state in any situation. In addition, the convergence rates of WINSTRON for three special data distributions will be derived. In order to realize WINSTRON, its array architecture with low hardware complexity and high computing speed is also detailed. Finally, simulation results are included to demonstrate its effectiveness and its advantages over three existing networks

Keywords

neural net architecture; parallel architectures; WINSTRON; array architecture; coarse-fine competition; competitive learning algorithm; computing speed; data distributions; hardware complexity; winners-take-all neural network; Analog circuits; Arithmetic; Artificial neural networks; Computational modeling; Computer architecture; Convergence; Hardware; Neural networks; Neurons; Pattern recognition;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.712163

Filename

712163