Analog MOS Circuit Systems Performing the Visual Tracking with Bio-Inspired Simple Networks
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
On the Computational Power of Max-Min Propagation Neural Networks
Neural Processing Letters
COMAX: A Cooperative Method for Determining the Position of the Maxima
Neural Processing Letters
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
A discrete-time dynamic K-winners-take-all neural circuit
Neurocomputing
IEEE Transactions on Neural Networks
Clustering: A neural network approach
Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
A model of analogue K-winners-take-all neural circuit
Neural Networks
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Presents a k-winners-take-all circuit that is an extension of the winner-take-all circuit by Lazzaro et al. (1989). The problem of selecting the largest k numbers is formulated as a mathematical programming problem whose solution scheme, based on the Lagrange multiplier method, is directly implemented on an analog circuit. The wire length in this circuit grows only linearly with the number of elements, and the circuit is more suitable for real-time processing than the Hopfield networks because the present circuit produces the solution almost instantaneously-in contrast to the Hopfield network, which requires transient convergence to the solution from a precise initial state. The selection resolution in the present circuit is, however, only finite in contrast to the almost infinite resolution in the Hopfield networks