Self-emergence of chaos in the identification of irregular periodic behavior.
Various forms of chaotic synchronization have been proposed as ways of realizing associative memories and/or pattern recognizers. To exploit this kind of synchronization phenomena in temporal pattern recognition, a chaotic dynamical system representing the class of signals that are to be recognized must be established. This system can be determined by means of identification techniques. The fulfillment of the chaotic condition could be imposed as a constraint. However, it is shown here that, even for a very simple identification algorithm, chaos emerges by itself, allowing us to model the diversity of nearly periodic signals.
De Feo
Oscar
O
Laboratory of Nonlinear Systems, Swiss Federal Institute of Technology Lausanne, EL-E, EPFL-I&C-LANOS, CH-1015 Lausanne, Switzerland.
D000465:Algorithms; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods; D020387:Food Chain; D008954:Models, Biological; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D010507:Periodicity; D011157:Population Dynamics; D013065:Speech Intelligibility / Q000502:physiology; D013067:Speech Perception / Q000502:physiology; D013068:Speech Production Measurement / Q000379:methods; D013269:Stochastic Processes