Neural Networks Backpropagation Chaotic Dynamic Systems Control Feedback Methods
Institute of Information Theories and Applications FOI ITHEA
Signal processing is an important topic in technological research today. In the areas of nonlinear
dynamics search, the endeavor to control or order chaos is an issue that has received increasing attention over
the last few years. Increasing interest in neural networks composed of simple processing elements (neurons) has
led to widespread use of such networks to control dynamic systems learning. This paper presents
backpropagation-based neural network architecture that can be used as a controller to stabilize unsteady periodic
orbits. It also presents a neural network-based method for transferring the dynamics among attractors, leading to
more efficient system control. The procedure can be applied to every point of the basin, no matter how far away
from the attractor they are. Finally, this paper shows how two mixed chaotic signals can be controlled using a
backpropagation neural network as a filter to separate and control both signals at the same time. The neural
network provides more effective control, overcoming the problems that arise with control feedback methods.
Control is more effective because it can be applied to the system at any point, even if it is moving away from the
target state, which prevents waiting times. Also control can be applied even if there is little information about the
system and remains stable longer even in the presence of random dynamic noise.