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...

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Main Authors: Tan, A.H., Godfrey, K.
Format: Article
Language:English
Published: 2004
Subjects:
Online Access:http://shdl.mmu.edu.my/2472/
http://shdl.mmu.edu.my/2472/1/1736.pdf
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author Tan, A.H.
Godfrey, K.
author_facet Tan, A.H.
Godfrey, K.
author_sort Tan, A.H.
building MMU Institutional Repository
collection Online Access
description 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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spelling mmu-24722011-08-22T01:48:49Z http://shdl.mmu.edu.my/2472/ Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure Tan, A.H. Godfrey, K. TA Engineering (General). Civil engineering (General) 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. 2004-06 Article NonPeerReviewed application/pdf en http://shdl.mmu.edu.my/2472/1/1736.pdf Tan, A.H. and Godfrey, K. (2004) Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure. IEEE Transactions on Instrumentation and Measurement, 53 (3). pp. 744-753. ISSN 0018-9456 http://dx.doi.org/10.1109/TIM.2004.827083 doi:10.1109/TIM.2004.827083 doi:10.1109/TIM.2004.827083
spellingShingle TA Engineering (General). Civil engineering (General)
Tan, A.H.
Godfrey, K.
Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
title Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
title_full Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
title_fullStr Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
title_full_unstemmed Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
title_short Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure
title_sort modeling of direction-dependent processes using wiener models and neural networks with nonlinear output error structure
topic TA Engineering (General). Civil engineering (General)
url http://shdl.mmu.edu.my/2472/
http://shdl.mmu.edu.my/2472/
http://shdl.mmu.edu.my/2472/
http://shdl.mmu.edu.my/2472/1/1736.pdf