Abstract
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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Liu, Q., Wang, J. (2006). A Recurrent Neural Network for Non-smooth Convex Programming Subject to Linear Equality and Bound Constraints. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_110
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DOI: https://doi.org/10.1007/11893257_110
Publisher Name: Springer, Berlin, Heidelberg
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