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A Method for Solving Nonsmooth Pseudoconvex Optimization

عنوان مقاله: A Method for Solving Nonsmooth Pseudoconvex Optimization
شناسه ملی مقاله: JR_IJMAC-12-1_002
منتشر شده در در سال 1401
مشخصات نویسندگان مقاله:

Maryam Bala Seyed Ghasir - Department of Mathematics, Payame Noor University (PNU), P.O. Box ۱۹۳۹۵-۴۶۹۷, Tehran, Iran
Aghileh Heydari - Department of Mathematics, Payame Noor University (PNU), P.O. Box ۱۹۳۹۵-۴۶۹۷, Tehran, Iran
Mohammad Ali Badamchizadeh - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

خلاصه مقاله:
In this paper, a two layer recurrent neural network (RNN) is shown for solving nonsmooth pseudoconvex optimization . First it is proved that the equilibrium point of the proposed neural network (NN) is equivalent to the optimal solution of the orginal optimization problem. Then, it is proved that the state of the proposed neural network is stable in the sense of Lyapunov, and convergent to an exact optimal solution of the original optimization. Finally two examples are given to illustrate the effectiveness of the proposed neural network.

کلمات کلیدی:
Recurrent neural network, nonsmooth pseudoconvex, Optimization, Global convergence

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1589923/