Title :
Recursive structure estimation for nonlinear identification with modular networks
Author :
Kadirkamanathan, Visakan ; Fabri, Simon G.
Author_Institution :
Dept. of Autom. Control & Syst. Eng., Sheffield Univ., UK
fDate :
31 Aug-2 Sep 1998
Abstract :
The paper presents a recursive nonlinear identification scheme with modular networks consisting of local linear models. New local linear models are added online as and when necessary. The algorithms is developed within a probabilistic framework and utilises the Kalman filter for estimation of model parameters. Simulated results demonstrate the operation of the algorithm
Keywords :
Kalman filters; filtering theory; neural nets; probability; recursive estimation; Kalman filter; local linear models; model parameter estimation; modular neural networks; nonlinear identification; probabilistic framework; recursive structure estimation; Adaptive control; Control systems; Integrated circuit modeling; Linear systems; Nonlinear dynamical systems; Nonlinear systems; Parameter estimation; Recursive estimation; System identification; Systems engineering and theory;
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.1998.710664
