Integrated OBF-NN models for extrapolation enhancement in conventional neural networks for nonlinear systems
Abstract In this paper the integration of linear and nonlinear models in parallel for nonlinear system identification is investigated. A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters (OBF) and a nonlinear feedforward (MLP) N...
محفوظ في:
| المؤلفون الرئيسيون: | , , , |
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| التنسيق: | Conference or Workshop Item |
| منشور في: |
2011
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://eprints.utp.edu.my/10748/1/HZabiri_aucc2011.pdf http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6114303&tag=1 http://eprints.utp.edu.my/10748/ |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Universiti Teknologi Petronas |
| الملخص: | Abstract In this paper the integration of linear and nonlinear models in parallel for nonlinear
system identification is investigated. A residuals-based sequential identification algorithm
using parallel integration of linear Orthornormal basis filters (OBF) and a nonlinear
feedforward (MLP) NN model is used and applied to the nonlinear Van de Vusse reactor.
Results show improved extrapolation capability of the proposed method in comparison to
conventional MLP NN, and opens up a promising area for further research and analysis. |
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