We consider the problem of locating a single new facility in the plane in the presence of a bounded region, where..

In this paper, we present a one-layer recurrent neural network (NN) for solving convex optimization problems by using the MangasarianandSolodov(MS)implicitLagrangianfunction.InthispaperbyusingKrush–Kuhn–TuckerconditionsandMS ...

In this paper we present a novel recurrent neural network for solving convex nonlinear

programming problems. The proposed neural network is derived based on an NCP function, and one

of the prominent features ...

In this paper we present that solution of convex programming problems is equivalent with solution of projection formulation, then we introduce neural network models for solving projection formulation and analysis stability conditions and convergence...

In this paper we propose a new and fast hybrid technique for pattern synthesis and peak side lobe (PSL) control of thinned array antennas. This technique is based on the using difference sets (DS) to thin an array and convex programming (CP...

This paper presents a gradient neural network model for solving convex nonlinear

programming (CNP) problems. The main idea is to convert the CNP problem into an equivalent

unconstrained minimization problem ...

. Then, we propose a convex program by using the

similar strategy proposed by Ji et al. (2010)....