DocumentCode :
3245669
Title :
Nonlinear system identification using spatiotemporal neural networks
Author :
Atiya, Amir ; Parlos, Alexander
Author_Institution :
Dynamica, Inc., Houston, TX, USA
Volume :
2
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
504
Abstract :
The so-called spatiotemporal neural network is considered. This is a neural network where the conventional weight multiplication operation is replaced by a linear filtering operation. A training algorithm is derived for such networks. The problem of nonlinear system identification is considered as an application for spatiotemporal networks. Nonlinear system identification is one of the problems in the systems area, with limited success for results based on conventional methods. Neural network approaches are encouraging, but further exploration is needed. The capability of the spatiotemporal neural networks to identify nonlinear systems is explored through a simple example using the derived learning rule. The simulation results are encouraging, though testing of the identification method on a real-world system is still under investigation
Keywords :
identification; learning (artificial intelligence); neural nets; nonlinear systems; identification method; learning rule; linear filtering operation; nonlinear system identification; spatiotemporal networks; spatiotemporal neural networks; system identification; training algorithm; weight multiplication operation; Linear systems; Maximum likelihood detection; Neural networks; Neurons; Nonlinear dynamical systems; Nonlinear systems; Parameter estimation; Power system modeling; Spatiotemporal phenomena; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.226938
Filename :
226938
Link To Document :
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