Title :
Reconstructed dynamics and chaotic signal modeling
Author :
Kuo, Jyh-Ming ; Principe, Jose C.
Author_Institution :
Dept. of Electr. Eng., Florida Univ., Gainesville, FL, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A nonlinear AR model is derived from the reconstructed dynamics of a signal. The underlying system is assumed to be nonlinear, autonomous, and deterministic. In this formulation. The output error scheme is shown to be more suitable than the equation error scheme in network training. A method to incorporate the information of dynamical invariants in signal modeling is proposed. Using this global information, the authors are able to avoid the oscillation problem in training a network to model chaotic time series
Keywords :
autoregressive processes; chaos; learning (artificial intelligence); neural nets; signal reconstruction; time series; chaotic signal modeling; dynamical invariants; equation error scheme; global information; network training; nonlinear AR model; output error scheme; reconstructed dynamics; Chaos; Delay effects; Frequency domain analysis; History; Multilayer perceptrons; Neural engineering; Nonlinear dynamical systems; Nonlinear equations; Predictive models; Testing;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374734
