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ITHEA Classification Structure > F. Theory of Computation  > F.1 COMPUTATION BY ABSTRACT DEVICES  > F.1.1 Models of Computation 
NEURAL CONTROL OF CHAOS AND APLICATIONS
By: Hernández et al. (3899 reads)
Rating: (1.00/10)

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.

Keywords: Neural Network, Backpropagation, Chaotic Dynamic Systems, Control Feedback Methods.

ACM Classification Keywords: F.1.1 Models of Computation: Self-modifying machines (neural networks); F.1.2 Modes of Computation: Alternation and nondeterminism; G.1.7 Ordinary Differential Equations: Chaotic systems; G.3 Probability and Statistics: Stochastic processes

Link:

NEURAL CONTROL OF CHAOS AND APLICATIONS

Cristina Hernández, Juan Castellanos, Rafael Gonzalo, Valentín Palencia

http://www.foibg.com/ijita/vol12/ijita12-2-p01.pdf

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F.1.1 Models of Computation
article: ALGORITHMIZATION PROCESS FOR FRACTAL ANALYSIS IN THE CHAOTIC DYNAMICS OF ... · INTELLIGENT TRADING SYSTEMS · AN ARCHITECTURE FOR REPRESENTING BIOLOGICAL PROCESSES BASED ON NETWORKS... · Polynomial Regression using a Perceptron with Axo-axonic Connections · ACCOUNTING IN THEORETICAL GENETICS · A NEW METHOD FOR THE BINARY ENCODING AND HARDWARE IMPLEMENTATION OF METABOLIC P · GENETIC BASED SPOT DETECTION METHOD IN TWO-DIMENSIONAL ELECTROPHORESIS IMAGES · Self-Organizing Architectural design based on Morphogenetic Programming · PRION CRYSTALIZATION MODEL AND ITS APPLICATION TO RECOGNITION PATTERN · POLYNOMIAL APPROXIMATION USING PARTICLE SWARM OPTIMIZATION OF LINEAR ... · MULTIPLE-MODEL DESCRIPTION AND STRUCTURE DYNAMICS ANALYSIS OF ACTIVE MOVING... · COMPUTATIONAL MODEL FOR SERENDIPITY · STRING MEASURE APPLIED TO STRING SELF-ORGANIZING MAPS AND NETWORKS OF ... · CLASSIFICATION OF DATA TO EXTRACT KNOWLEDGE FROM NEURAL NETWORKS · SIMULTANEOUS CONTROL OF CHAOTIC SYSTEMS USING RBF NETWORKS · TIMED TRANSITION AUTOMATA AS NUMERICAL PLANNING DOMAIN · STATIC ANALYSIS OF USEFULNESS STATES IN TRANSITION P SYSTEMS · GENERALIZING OF NEURAL NETS: FUNCTIONAL NETS OF SPECIAL TYPE · AUTOMATA–BASED METHOD FOR SOLVING SYSTEMS OF LINEAR CONSTRAINTS IN {0,1} · FILTERED NETWORKS OF EVOLUTIONARY PROCESSORS* · NEURAL CONTROL OF CHAOS AND APLICATIONS · SOLVING A DIRECT MARKETING PROBLEM BY THREE TYPES OF ARTMAP NEURAL NETWORKS ·
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