Title

Parametric Sensitivity and Scalability of k-Winners-Take-All Networks with a Single State Variable and Infinity-Gain Activation Functions

Abstract

In recent years, several k-winners-take-all (kWTA) neural networks were developed based on a quadratic programming formulation. In particular, a continuous-time kWTA network with a single state variable and its discrete-time counterpart were developed recently. These kWTA networks have proven properties of global convergence and simple architectures. Starting with problem formulations, this paper reviews related existing kWTA networks and extends the existing kWTA networks with piecewise linear activation functions to the ones with high-gain activation functions. The paper then presents experimental results of the continuous-time and discrete-time kWTA networks with infinity-gain activation functions. The results show that the kWTA networks are parametrically robust and dimensionally scalable in terms of problem size and convergence rate.

Meeting Name

7th International Symposium on Neural Networks, ISNN 2010 (2010: Jun. 6-9, Shanghai, China)

Department(s)

Computer Science

Sponsor(s)

Research Grants Council of the Hong Kong Special Administrative Region, China

Comments

The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project no. CUHK417608E).

Keywords and Phrases

K Winners-Take-All; Optimization; Parametric Sensitivity; Recurrent Neural Networks; Scalability

International Standard Book Number (ISBN)

978-364213277-3

International Standard Serial Number (ISSN)

0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2010 Springer Verlag, All rights reserved.

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