Title :
Nonlinear state space learning with EM and neural networks
Author :
De Freitas, Joiio Fg ; Niranjan, Mahesan ; Gee, Andrew H.
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
fDate :
31 Aug-2 Sep 1998
Abstract :
In this paper, we derive the EM algorithm for nonlinear state space models. We show how this algorithm, in conjunction with the well known techniques of Kalman smoothing, can be used for nonlinear system identification. A multilayer perceptron, whose derivatives are computed by backpropagation, is used to generate the measurements mapping. We found that the methodic is intrinsically very powerful, simple, elegant and stable. However, it exhibits very slow convergence
Keywords :
Kalman filters; backpropagation; convergence; identification; maximum likelihood estimation; multilayer perceptrons; nonlinear systems; smoothing methods; state-space methods; EM algorithm; Kalman smoothing; backpropagation; convergence; measurements mapping; multilayer perceptron; neural networks; nonlinear state space learning; nonlinear system identification; Covariance matrix; Hidden Markov models; Inference algorithms; Kalman filters; Neural networks; Power system modeling; Smoothing methods; State-space methods; Switches; Working environment noise;
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.1998.710655
