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Título

Nonlinear dynamics extraction for time-delay systems using modular neural networks synchronization and prediction

Autor Ortín González, Silvia ; Gutiérrez, José M. ; Pesquera, Luis ; Vasquez, Hernando
Palabras clave Functional networks
Neural networks
Delayed chaotic systems
Fecha de publicación 2005
EditorElsevier
Citación Physica A: Statistical Mechanics and its Applications 351(1): 133-141 (2005)
ResumenIt is shown that the nonlinear dynamics of chaotic time-delay systems can be reconstructed using a new type of neural network with two modules: one for nonfeedback part with input data delayed by the embedding time, and a second one for the feedback part with input data delayed by the feedback time. The method is applied to both simulated and experimental data from an electronic analog circuit of the Mackey–Glass system. Better results are obtained for the modular than for feedforward neural networks for the same number of parameters. It is found that the complexity of the neural network model required to reconstruct nonlinear dynamics does not increase with the delay time. Synchronization between the data and the model with diffusive coupling is also achieved. We have also shown by iterating the model from the present point that the dynamics can be predicted with a forecast horizon larger than the feedback delay time.
URI http://hdl.handle.net/10261/94107
DOI10.1016/j.physa.2004.12.015
Identificadoresdoi: 10.1016/j.physa.2004.12.015
issn: 0378-4371
Aparece en las colecciones: (IFCA) Artículos
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