DocumentCode :
2961321
Title :
A computational intelligence technique for the identification of non-linear non-stationary systems
Author :
Turchetti, Claudio ; Gianfelici, Francesco ; Biagetti, Giorgio ; Crippa, Paolo
Author_Institution :
Dipt. di Elettron., Univ. Politec. delle Marche, Ancona
fYear :
2008
fDate :
1-8 June 2008
Firstpage :
3034
Lastpage :
3038
Abstract :
This paper addresses nonlinear nonstationary system identification from stimulus-response data, a problem concerning a large variety of applications, in dynamic control as well as in signal processing, communications, physiological system modelling and so on. Among the different methods suggested in the vast literature for nonlinear system modelling, the ones based on the Volterra series and the Neural Networks are the most commonly used. However, a strong limitation for the applicability of these methods lies in the necessary property of stationarity, an assumption that cannot be considered as valid in general and strongly affecting the validity of results. Another weakness of the approaches currently used is that they refer to differential systems, thus being unsuitable to model systems described by integral equations. A computational intelligence technique that exploits the potentialities of both the Karhunen-Loeve Transform (KLT) and Neural Networks (NNs) representation and without any of the limitations of the previous approaches is suggested in this paper. The technique is suitable for modelling the wide class of systems described by nonlinear nonstationary functionals, thus including both differential and integral systems. It takes advantage of the KLT separable kernel representation that is able to separate the dynamic and static behaviours of the system as two distinct components, and the approximation property of NNs for the identification of the nonlinear no-memory component. To validate the suggested technique comparisons with experimental results on both nonlinear nonstationary differential and integral systems are reported.
Keywords :
Karhunen-Loeve transforms; Volterra series; data handling; neural nets; Karhunen-Loeve transform; Volterra series; computational intelligence technique; integral equations; neural networks; nonlinear no-memory component identification; nonlinear non-stationary systems identification; nonlinear system modelling; stimulus-response data; Communication system control; Computational intelligence; Integral equations; Karhunen-Loeve transforms; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Signal processing; System identification; Karhunen-Loève Transform (KLT); Lee-Schetzen Method; Non-Linear Non-Stationary System Identification (NLNSSI); Polynomial Approximation; Statistical Signal Processing; Volterra Series (VS);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2008.4634226
Filename :
4634226
Link To Document :
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