Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
On the K-winners-take-all-network
Advances in neural information processing systems 1
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Winner-Takes-All Associative Memory: A Hamming DistanceVector Quantizer
Analog Integrated Circuits and Signal Processing
On a Constant-Time, Low-Complexity Winner-Take-All Neural Network
IEEE Transactions on Computers
Analog Integrated Circuits and Signal Processing
On the Computational Power of Winner-Take-All
Neural Computation
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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new k-winners-take-all neural network and its array architecture
IEEE Transactions on Neural Networks
Analysis for a class of winner-take-all model
IEEE Transactions on Neural Networks
Another K-winners-take-all analog neural network
IEEE Transactions on Neural Networks
Performance analysis for a K-winners-take-all analog neural network: basic theory
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
K-winners-take-all circuit with O(N) complexity
IEEE Transactions on Neural Networks
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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.