Nonlinear system identification using integrated linear-NN models: series vs. parallel structures
In this paper, the performance of integrated linear-NN models is investigated for nonlinear system identification using two different structures: series vs. parallel. In particular, Laguerre filters are selected as the linear models, and multi-layer perceptron (MLP) or feed-forward neural networks...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/10747/1/7-ICMSC2011S021.pdf http://www.ipcsit.com/vol10/7-ICMSC2011S021.pdf http://eprints.utp.edu.my/10747/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | In this paper, the performance of integrated linear-NN models is investigated for nonlinear
system identification using two different structures: series vs. parallel. In particular, Laguerre filters are
selected as the linear models, and multi-layer perceptron (MLP) or feed-forward neural networks (NN) are
selected for the nonlinear models. Results show promising capability of the (novel) parallel Laguerre-NN
structure especially in terms of its generalization capability when subjected to data different from those used
during the identification stage in comparison to the series Laguerre-NN.
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