A 2D approach to tomographic image reconstruction using a Hopfield-type neural network
Artificial Intelligence in Medicine
Neural-Memory Based Control of Micro Air Vehicles (MAVs) with Flapping Wings
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Robust Model Predictive Control Using a Discrete-Time Recurrent Neural Network
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Engineering Applications of Artificial Intelligence
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
Solving convex optimization problems using recurrent neural networks in finite time
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A new one-layer neural network for linear and quadratic programming
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A new neural network for solving nonlinear programming problems
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
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
A model of analogue K-winners-take-all neural circuit
Neural Networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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The design, analysis, and application of a new recurrent neural network for quadratic programming, called simplified dual neural network, are discussed. The analysis mainly concentrates on the convergence property and the computational complexity of the neural network. The simplified dual neural network is shown to be globally convergent to the exact optimal solution. The complexity of the neural network architecture is reduced with the number of neurons equal to the number of inequality constraints. Its application to k-winners-take-all (KWTA) operation is discussed to demonstrate how to solve problems with this neural network