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Optica Publishing Group
  • Quantum Electronics and Laser Science Conference
  • OSA Technical Digest (Optica Publishing Group, 1991),
  • paper QTuI43

Neural network algorithm for efficient characterization and forecasting from time series of nonlinear-optical systems

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Abstract

Recent interest and new techniques in the analysis of chaotic time series have been reviewed by Farmer and Sidorowich,1,2 and the capability of neural-network computational algorithms for forecasting of chaotic time series has been demonstrated by Lapedes and Färber.3 We have used the two-hidden-layer backpropagation neuralnetwork algorithm to analyze stationary time series generated by using the Lorentz model coupled nonlinear differential equations for the single-mode laser in the chaotic regime of the parameter space, as well as the Buffing's oscillator differential equation for parameters corresponding to the dynamical region of ionization.

© 1991 Optical Society of America

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