A posteriori error bounds for the linearly-constrained varitional inequality problem
Mathematics of Operations Research
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Foundations of robotics: analysis and control
Foundations of robotics: analysis and control
A deterministic annealing neural network for convex programming
Neural Networks
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Computers & Mathematics with Applications
A dual neural network for kinematic control of redundant robotmanipulators
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 general methodology for designing globally convergent optimization neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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A new neural network model is proposed for solving nonlinear optimization problems with a general form of linear constraints. Linear constraints, which may include equality, inequality and bound constraints, are considered to cover the need for engineering applications. By employing this new model in image fusion algorithm, an optimal fusion vector is exploited to enhance the quality of fused images efficiently. The stability and convergence analysis of the novel model are proved in details. The simulation examples are used to demonstrate the validity of the proposed model.