On the inverse kinematics of redundant manipulators
International Journal of Robotics Research
Foundations of robotics: analysis and control
Foundations of robotics: analysis and control
Modified Projection-Type Methods for Monotone Variational Inequalities
SIAM Journal on Control and Optimization
An Exterior Newton Method for Strictly Convex Quadratic Programming
Computational Optimization and Applications
A New Algorithm for Solving Strictly Convex Quadratic Programs
SIAM Journal on Optimization
Design methodology and stability analysis of recurrent neural networks for constrained optimization
Design methodology and stability analysis of recurrent neural networks for constrained optimization
Journal of Global Optimization
An Extended Projection Neural Network for Constrained Optimization
Neural Computation
On the Computational Power of Winner-Take-All
Neural Computation
Constrained multi-variable generalized predictive control using a dual neural network
Neural Computing and Applications
A dual neural network for kinematic control of redundant robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A delayed neural network for solving linear projection equations and its analysis
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
K-winners-take-all circuit with O(N) complexity
IEEE Transactions on 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
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This paper proposes a novel neural dynamical approach to a class of convex quadratic programming problems where the number of variables is larger than the number of equality constraints. The proposed continuous-time and proposed discrete-time neural dynamical approach are guaranteed to be globally convergent to an optimal solution. Moreover, the number of its neurons is equal to the number of equality constraints. In contrast, the number of neurons in existing neural dynamical methods is at least the number of the variables. Therefore, the proposed neural dynamical approach has a low computational complexity. Compared with conventional numerical optimization methods, the proposed discrete-time neural dynamical approach reduces multiplication operation per iteration and has a large computational step length. Computational examples and two efficient applications to signal processing and robot control further confirm the good performance of the proposed approach.