Characterization of optical instabilities and chaos using fast multilayer perceptron training algorithms

A new and novel training algorithm, based upon the matrix pseudoinverse least-squares method, is introduced for training hidden layer, forward-feed neural networks with high accuracy and speed for nonlinear and chaotic time series prediction. Model-generated chaotic time series, including that of the Lorenz system, are used to measure performance and robustness. Our new training algorithm has rendered application of forward-feed, hidden-layer neural networks for adaptive chaotic time series analysis, as well as other signal processing, practical and near real time using standard desktop computation facilities. We have applied our method, in conjunction with other standard methods, to the analysis of stimulated Brillouin scattering under cw pump conditions involving a single Stokes and pump signal in a single- mode optical fiber as the nonlinear medium. We use Stokes signal data generated from a standard model and correlate the training performance of our algorithm with statistical and dynamical characteristics of the system determined by other means.