Parallel and Distributed Computation: Numerical Methods
Parallel and Distributed Computation: Numerical Methods
Multilayer perceptron for nonlinear programming
Computers and Operations Research
Convex Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new neural network for solving linear programming problems and its application
IEEE Transactions on Neural Networks
A high-performance neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
A Lagrangian network for kinematic control of redundant robot manipulators
IEEE Transactions on Neural Networks
A high-performance feedback neural network for solving convex nonlinear programming problems
IEEE Transactions on Neural Networks
A neural network for a class of convex quadratic minimax problems with constraints
IEEE Transactions on Neural Networks
A recurrent neural network for solving nonlinear convex programs subject to linear constraints
IEEE Transactions on Neural Networks
A Neural Network Model for Solving Nonlinear Optimization Problems with Real-Time Applications
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
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In this paper linear and quadratic programming problems are solved using a novel recurrent artificial neural network. The new model is simpler and converges very fast to the exact primal and dual solutions simultaneously. The model is based on a nonlinear dynamical system, using arbitrary initial conditions. In order to construct an economy model, here we avoid using analog multipliers. The dynamical system is a time dependent system of equations with the gradient of specific Lyapunov energy function in the right hand side. Block diagram of the proposed neural network model is given. Fourth order Runge-Kutta method with controlled step size is used to solve the problem numerically. Global convergence of the new model is proved, both theoretically and numerically. Numerical simulations show the fast convergence of the new model for the problems with a unique solution or infinitely many. This model converges to the exact solution independent of the way that we may choose the starting points, i.e. inside, outside or on the boundaries of the feasible region.