Adaptive signal processing techniques for chaotic systems

Abstract

The issue of modeling chaotic systems is addressed. Present methods for treating chaotic dynamics are based on state space reconstruction through delay embedding. These approaches are computationally intensive and are adversely affected by noise in the experimental time series. The authors take a different approach and apply an adaptive layered structure for estimation of chaotic dynamics. They show that presently used spatial local approximations are not necessary and that their temporal adaptive local approximations perform better, are tolerant to noise factors, and save an order of magnitude in computations, and data requirements.<>