سال انتشار: ۱۳۹۱

محل انتشار: کنفرانس بین المللی مدل سازی غیر خطی و بهینه سازی

تعداد صفحات: ۶

نویسنده(ها):

Najmeh Hosseinipour-Mahani – Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University
Alaeddin Malek –

چکیده:

In this paper, we propose a projection neural network model for solving a class of smooth nonconvex optimization problems where the feasible set is convex but the objective function is not convex. Compared with the existing neural network models for solving nonconvex quadratic problems, this neural network model canbe applied to solve problems that local optima need not be global optima. Simulation results are given to illustrate the global convergence and performance of the proposed model for nonconvex quadratic optimization problems with quadratic constraint