Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Foundations of robotics: analysis and control
Foundations of robotics: analysis and control
On the stability of globally projected dynamical systems
Journal of Optimization Theory and Applications
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
IEEE Transactions on Signal Processing
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
A neural network for a class of convex quadratic minimax problems with constraints
IEEE Transactions on Neural Networks
Neural network for quadratic optimization with bound constraints
IEEE Transactions on Neural Networks
Solving linear programming problems with neural networks: a comparative study
IEEE Transactions on Neural Networks
A neural network for robust LCMP beamforming
Signal Processing - Fractional calculus applications in signals and systems
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Evaluating the physical realism of character animations using musculoskeletal models
MIG'10 Proceedings of the Third international conference on Motion in games
A dynamical model for solving degenerate quadratic minimax problems with constraints
Journal of Computational and Applied Mathematics
A capable neural network model for solving the maximum flow problem
Journal of Computational and Applied Mathematics
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This paper develops an improved neural network to solve convex quadratic optimization problems with general linear constraints. Compared with the existing primal-dual neural network and dual neural network for solving such problems, the proposed neural network has a lower complexity for implementation. Unlike the Kennedy-Chua neural network, the proposed neural network can converge to an exact optimal solution. Analyzed results and illustrative examples show that the proposed neural network has a fast convergence to the optimal solution. Finally, the proposed neural network is effectively applied to real-time beamforming.