Technology for data acquisition in diagnosis processes by means of the identification using Volterra models

Author

Vitaliy, Pavlenko ; Oleksandr, Fomin ; Vladimir, Ilyin

Author_Institution

Dept. of Comput. Control Syst., Odessa Nat. Polytech. Univ., Odessa, Ukraine

fYear

2009

fDate

21-23 Sept. 2009

Firstpage

327

Lastpage

332

Abstract

The method of a black-box diagnostics, founded on nonparametric identification of objects using integro-power Volterra series is offered. It provides a set of diagnostic features formed on base of multidimensional Volterra kernels: discrete values of Volterra kernels, heuristic features, moments and wavelet transform coefficients. It is researched a self-descriptiveness of provided features using classifier on base of neural nets. The diagnostic spaces are formed by method of all features combination selection.

Keywords

data acquisition; integro-differential equations; neural nets; pattern classification; wavelet transforms; Volterra models identification; black-box diagnostics; data acquisition; feature classification; integro-power Volterra series; multidimensional Volterra kernels; neural nets; wavelet transform; Application software; Conferences; Control system synthesis; Data acquisition; Discrete wavelet transforms; Kernel; Multidimensional systems; Neural networks; Power system modeling; Production; Model diagnostics; Volterra kernels; Volterra series; nonparametric identification; self-descriptiveness; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2009. IDAACS 2009. IEEE International Workshop on

Conference_Location

Rende

Print_ISBN

978-1-4244-4901-9

Electronic_ISBN

978-1-4244-4882-1

Type

conf

DOI

10.1109/IDAACS.2009.5342968

Filename

5342968