A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

Abstract

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.