DISCRETE RECONSTRUCTION OF STRANGE ATTRACTORS OF CHUA CIRCUIT

This work is concerned with the identification of discrete nonlinear models from time series of strange attractors emerging in Chua's circuit. The quality of the models is assessed by estimating the largest Lyapunov exponent, the correlation dimension and by comparing the geometry of the reconstructed dynamics to the original attractors. The major aim of the paper is to investigate how parameters such as the discretization period, sampling rate and the number of terms in an estimated model affect the dynamics and to determine if there is any relationship among such parameters. Conclusions in this direction are believed to be especially relevant in determining adequate model structures in identification applications. This is known to be critical in the identification of nonlinear systems.