The Performance of the Stochastic DNN-kWTA Network

Recently, the dual neural network (DNN) model has been used to synthesize the k-winners-take-all (kWTA) process. The advantage of this DNN-kWTA model is that its structure is very simple. It contains 2n + 1 connections only. Also, the convergence behavior of the DNN-kWTA model under the noise condit...

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Bibliographic Details
Published inNeural Information Processing pp. 279 - 286
Main Authors Feng, Ruibin, Leung, Chi-Sing, Ng, Kai-Tat, Sum, John
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Energy Function
Equilibrium Point
Model Performance
Network Dynamic
Output Node
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Summary:Recently, the dual neural network (DNN) model has been used to synthesize the k-winners-take-all (kWTA) process. The advantage of this DNN-kWTA model is that its structure is very simple. It contains 2n + 1 connections only. Also, the convergence behavior of the DNN-kWTA model under the noise condition was reported. However, there is no an analytic expression on the equilibrium point. Hence it is difficult to study how the noise condition affects the model performance. This paper studies how the noise condition affects the model performance. Based on the energy function, we propose an efficient method to study the performance of the DNN-kWTA model under the noise condition. Hence we can efficiently study how the noise condition affects the model performance.
ISBN:3319126369
9783319126364
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-12637-1_35