Matrix theory: a second course
Matrix theory: a second course
On the K-winners-take-all-network
Advances in neural information processing systems 1
Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
The basins of attraction of a new Hopfield learning rule
Neural Networks
Global convergence rate of recurrently connected neural networks
Neural Computation
Time Evaluation for WTA Hopfield Type Circuits Affected by Cross-Coupling Capacitances
Advances in Neuro-Information Processing
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Global exponential stability of impulsive neural networks with variable delay: an LMI approach
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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
IEEE Transactions on Neural Networks
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A continuous time neural network built with non-linear amplifiers which selects the largest item of a list (WT A) is considered. The network receives and processes lists admitted one by one. If the processing and resetting times are imposed, our paper gives a method to find the circuit parameters assuring a correct operation. We take into account the capacitive coupling between input terminals and present complete existence and convergence results on the differential model. The main achievement consists of simple bounds for the processing and resetting times. They are inferred by an original method of decoupling the system model into solvable linear differential inequalities. Also, a new procedure to impose the stationary WT A state is given. All these results are valid under various parameter restrictions. They lead to a neat design procedure which starts from imposed processing and resetting time and list density to determine the WT A threshold, the interconnection conductance, the amplifier gain, the bias current. Numerical examples check and interpret the results.