in this paper, we introduce a hybrid approach based on neural network and optimization teqnique to solve ordinary differential equation. in proposed model we use heyperbolic secont transformation function in hiden layer of neural network part and bfgs teqnique in optimization part. in comparison with existing similar neural networks proposed model provides solutions with high accuracy. numerica...

Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

‎By p-power (or partial p-power) transformation‎, ‎the Lagrangian function in nonconvex optimization problem becomes locally convex‎. ‎In this paper‎, ‎we present a neural network based on an NCP function for solving the nonconvex optimization problem‎. An important feature of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimizatio...

nowadays, the environmental impact of transportation project and, especially air pollution impacts, are major concerns in evaluating transportation projects. based on this concern, beside definition of traditional objective functions like total travel time and total investment cost, different type of environmental related function is considered as objective function in urban network design. in ...

This paper presents an efficient optimization procedure to find the optimal shapes of double curvature  arch  dams  considering  fluid–structure  interaction  subject  to  earthquake  loading. The optimization is carried out using a combination of the magnetic charged system search, big bang-big crunch algorithm and artificial neural network methods. Performing the finite element  analysis  dur...

transmission network expansion planning (tnep) is an important component of power system planning. itdetermines the characteristics and performance of the future electric power network and influences the powersystem operation directly. different methods have been proposed for the solution of the static transmissionnetwork expansion planning (stnep) problem till now. but in all of them, stnep pr...