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Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/790

Title: Neural Control of Chaos and Aplications
Authors: Hernandez, Cristina
Castellanos, Juan
Gonzalo, Rafael
Palencia, Valentin
Keywords: Neural Networks
Backpropagation
Chaotic Dynamic Systems
Control Feedback Methods
Issue Date: 2005
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: 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.
URI: http://hdl.handle.net/10525/790
ISSN: 1313-0463
Appears in Collections:Volume 12 Number 2

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