Aspects of neural modeling of multidimensional chaotic attractors

This article discusses various aspects of neural modeling of multidimensional chaotic attractors. The Lorenz and Rosler attractors are considered as representative cases and are thoroughly examined. These two dynamical systems are expressed, within acceptable accuracy limits, by the corresponding systems of difference equations. Initially, the complete neural models of the attractors are examined. In this case, the neural networks are supplied with the values X_n , Y_n , Z_n of the systems to predict all the next components (X_{n +1}, Y_{n +1}, and Z_{n +1}) of the attractors. In the second case, named ‘component simulation’, the neural models are trained to predict only one of the values X_n , Y_n , Z_n , when they are fed with the complete input vector as in the first case. In the third case, the proposed neural networks are trained to predict only one component (X_{n +1}, Y_{n +1}, or Z_{n +1}) of the attractors, given a number of past values of the same component. Finally, the ability of the networks to predict the Y and Z components of an X time series of the dynamical systems is examined. Since the response of some networks is not satisfactory, the distribution of absolute error is considered in order to form a realistic picture of the networks’ performance.