Modeling of Direction-Dependent Processes Using Wiener Models and Neural Networks With Nonlinear Output Error Structure

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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Official URL: http://dx.doi.org/10.1109/TIM.2004.827083

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
URI: http://shdl.mmu.edu.my/id/eprint/2472

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