A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints

@article{Liang2000ARN,
  title={A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints},
  author={Xue-Bin Liang and Jun Wang},
  journal={IEEE transactions on neural networks},
  year={2000},
  volume={11 6},
  pages={1251-62}
}
This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense that any optimum of the objective function with bound constraints is also an equilibrium point of the… CONTINUE READING
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