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
Another Simple Recurrent Neural Network for Quadratic and Linear Programming
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A discrete-time dynamic K-winners-take-all neural circuit
Neurocomputing
IEEE Transactions on Neural Networks
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A new one-layer neural network for linear and quadratic programming
IEEE Transactions on Neural Networks
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Design of recurrent neural networks for solving constrained least absolute deviation problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Solving the assignment problem with the improved dual neural network
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Robotics and Computer-Integrated Manufacturing
A model of analogue K-winners-take-all neural circuit
Neural Networks
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This paper presents a novel recurrent neural network for solving a class of convex quadratic programming (QP) problems, in which the quadratic term in the objective function is the square of the Euclidean norm of the variable. This special structure leads to a set of simple optimality conditions for the problem, based on which the neural network model is formulated. Compared with existing neural networks for general convex QP, the new model is simpler in structure and easier to implement. The new model can be regarded as an improved version of the dual neural network in the literature. Based on the new model, a simple neural network capable of solving the $k$-winners-take-all ( $k$-WTA) problem is formulated. The stability and global convergence of the proposed neural network is proved rigorously and substantiated by simulation results.