Title :
On-line EM algorithm and reconstruction of chaotic dynamics
Author :
Ishii, Shin ; Sato, Masa-aki
Author_Institution :
Nara Inst. of Sci. & Technol., Japan
fDate :
31 Aug-2 Sep 1998
Abstract :
We previously (1998) proposed an online EM algorithm for the normalized Gaussian network model, which is a network of local linear regression units. In this paper, we apply our approach to an identification problem of unknown nonlinear dynamics. Our approach is able to reconstruct the dynamics in shorter learning steps than approaches based on the recurrent neural network model. Even when dynamical variables can partially be observed, our approach is able to well reproduce the trajectory of the observed variables
Keywords :
Gaussian processes; chaos; estimation theory; neural nets; nonlinear dynamical systems; optimisation; statistical analysis; chaotic dynamics reconstruction; estimation/maximisation algorithm; local linear regression units; neural network; normalized Gaussian network model; online EM algorithm; unknown nonlinear dynamics; Chaos; Covariance matrix; Electronic mail; Humans; Information processing; Laboratories; Partitioning algorithms; Recurrent neural networks; Stochastic processes; Vectors;
Conference_Titel :
Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
Conference_Location :
Cambridge
Print_ISBN :
0-7803-5060-X
DOI :
10.1109/NNSP.1998.710666
