Hopfield neural networks: a survey
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A discrete-time dynamic K-winners-take-all neural circuit
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Gradient Like Behavior and High Gain Design of KWTA Neural Networks
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Clustering: A neural network approach
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Dynamic analysis of a general class of winner-take-all competitive neural networks
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A model of analogue K-winners-take-all neural circuit
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An analog Hopfield type neural network is given, that identifies the K largest components of a list d of N real numbers. The neurons are identical, with a tanh characteristic, and the weight matrix is symmetric and fully filled. The list to be processed is a summand of the input currents of the neurons, and the network is started from zero. We provide easily computable restrictions on the parameters. The main emphasis here is on the magnitude of the neuronal gain. A complete mathematical analysis is given. The trajectories are shown to eventually have positive components precisely in the positions given by the K largest elements in the input list