%0 Journal Article
%T Predicting Chaos
%A Sorin VLAD
%J Journal of Applied Computer Science & Mathematics
%D 2012
%I Stefan cel Mare University of Suceava
%X The main advantage of detecting chaos is that the time series is short term predictable. The prediction accuracy decreases in time. A strong evidence of chaotic dynamics is the existence of a positive Lyapunov exponent (i.e. sensitivity to initial conditions). In chaotic time series prediction theory the methods used can be placed in two classes: global and local methods. Neural networks are global methods of prediction. The paper tries to find a relation between the two parameters used in reconstruction of the state space (embedding dimension m and delay time ¦Ó) and the number of input neurons of a multilayer perceptron (MLP). For two of three time series studied, the minimum absolute error value is minimum for a MLP with the number of inputs equal to m*¦Ó.
%K Chaos Theory
%K Time Series
%K Chaos Identification
%K Prediction
%U jacs.usv.ro/getpdf.php?paperid=13_12