2008 Special Issue: Two k-winners-take-all networks with discontinuous activation functions

Authors:
Qingshan Liu;Jun Wang
Affiliations:
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong;Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
Venue:
Neural Networks
Year:
2008

Citing 14
Cited 9

Introduction to the theory of neural computation

Introduction to the theory of neural computation
Differential Inclusions: Set-Valued Maps and Viability Theory

Differential Inclusions: Set-Valued Maps and Viability Theory
Dynamical Behaviors of Delayed Neural Network Systems with Discontinuous Activation Functions

Neural Computation
On the Computational Power of Winner-Take-All

Neural Computation
A one-layer recurrent neural network with a discontinuous activation function for linear programming

Neural Computation
A new neural network for solving linear and quadratic programming problems

IEEE Transactions on Neural Networks
Another K-winners-take-all analog neural network

IEEE Transactions on Neural Networks
Current-mode subthreshold MOS circuits for analog VLSI neural systems

IEEE Transactions on Neural Networks
K-winner networks

IEEE Transactions on Neural Networks
Performance analysis for a K-winners-take-all analog neural network: basic theory

IEEE Transactions on Neural Networks
A general projection neural network for solving monotone variational inequalities and related optimization problems

IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application

IEEE Transactions on Neural Networks
Neural network for solving linear programming problems with bounded variables

IEEE Transactions on Neural Networks
K-winners-take-all circuit with O(N) complexity

IEEE Transactions on Neural Networks

A Discrete-Time Recurrent Neural Network with One Neuron for k-Winners-Take-All Operation

ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
A novel recurrent neural network with one neuron and finite-time convergence for k-winners-take-all operation

IEEE Transactions on Neural Networks
Analysis and design of a k-winners-take-all model with a single state variable and the heaviside step activation function

IEEE Transactions on Neural Networks
A novel recurrent neural network with finite-time convergence for linear programming

Neural Computation
Parametric sensitivity and scalability of k-winners-take-all networks with a single state variable and infinity-gain activation functions

ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Global exponential stability of discrete-time recurrent neural network for solving quadratic programming problems subject to linear constraints

Neurocomputing
A class of finite-time dual neural networks for solving quadratic programming problems and its k-winners-take-all application

Neural Networks
A model of analogue K-winners-take-all neural circuit

Neural Networks
A neural network with a single recurrent unit for associative memories based on linear optimization

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents two k-winners-take-all (k-WTA) networks with discontinuous activation functions. The k-WTA operation is first converted equivalently into linear and quadratic programming problems. Then two k-winners-take-all networks are designed based on the linear and quadratic programming formulations. The networks are theoretically guaranteed to be capable of performing the k-WTA operation in real time. Simulation results show the effectiveness and performance of the networks.