Abstract
In this paper, a Hopfiled neural network for nonlinear constrained optimization problem is discussed. The energy function for the nonlinear neural network with its neural dynamics is defined based on penalty function with two-order continuous differential. The system of the neural network is stable, and its equilibrium point of the neural dynamics is also an approximately solution for nonlinear constrained optimization problem. Based on the relationship between the equilibrium points and the energy function, an algorithm is developed for computing an equilibrium point of the system or an optimal solution to its optimization problem. The efficiency of the algorithm is illustrated with the numerical examples.
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Meng, Z., Dang, C. (2005). A Hopfiled Neural Network for Nonlinear Constrained Optimization Problems Based on Penalty Function. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_114
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DOI: https://doi.org/10.1007/11427391_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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