Controlling chaotic nonlinear dynamical systems

Controlling chaos is a concept which many researchers have recently focused their attention on. This paper proposes a back propagation neural network to control chaotic trajectories belonging to discrete-time systems in order to obtain their unsteady equilibrium and their limit cycles (multi-period orbits); improving problems that arise in other control methods, being equally effective when dealing with relatively small random dynamical noise. The obtained results have been very successful especially when dealing with chaotic dynamical systems.