Citation

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

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Abstract

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.

Item Type:	Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	22 Aug 2011 01:48
Last Modified:	22 Aug 2011 01:48
URII:	http://shdl.mmu.edu.my/id/eprint/2472

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