A Recurrent Neural Network for Non-smooth Convex Programming Subject to Linear Equality and Bound Constraints
نویسندگان
چکیده
In this paper, a recurrent neural network model is proposed for solving non-smooth convex programming problems, which is a natural extension of the previous neural networks. By using the non-smooth analysis and the theory of differential inclusions, the global convergence of the equilibrium is analyzed and proved. One simulation example shows the convergence of the presented neural network.
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