Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
The modeling of direction-dependent dynamic processes using Wiener models and recurrent neural network models with nonlinear output error structure is considered. The results obtained are compared for several simulated first-order and second-order processes and using three different types of input s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2472/ http://shdl.mmu.edu.my/2472/1/1736.pdf |
| Summary: | The modeling of direction-dependent dynamic processes using Wiener models and recurrent neural network models with nonlinear output error structure is considered. The results obtained are compared for several simulated first-order and second-order processes and using three different types of input signals: a pseudorandom binary signal, an inverse-repeat pseudorandom binary signal and a multisine (sum of harmonics) signal. Experimental results on a real system, namely an electronic nose system, are also presented to illustrate the applicability of the techniques discussed. |
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