PLS -WNN algorithm and its applications in aerodynamic parameters regression estimate

Author

Meng Yuebo ; Liu Guanghui

Author_Institution

Inf. & Control Eng. Sch., Xi´an Univ. of Archit. & Technol., Xi´an, China

fYear

2013

Firstpage

1990

Lastpage

1993

Abstract

An aerodynamic parameters regression estimate method based on Wavelet Neural Network by Partial Least Square feature extraction is proposed. This method can overcome problems of data noise and multiple correlations among parameters, accurately describe the dynamic characteristics of flight vehicle. Firstly, using Partial Least Square extracts basic feature of training samples in flight data. The second, aerodynamic parameters are regression estimated based on Wavelet Neural Network by using basic feature extracted. Finally, the method proved by experiment is effective and feasible to be used for flight vehicle aerodynamic parameters regression estimate.

Keywords

aerodynamics; aircraft; feature extraction; least squares approximations; mechanical engineering computing; neural nets; parameter estimation; regression analysis; vehicle dynamics; wavelet transforms; PLS-WNN algorithm; aerodynamic parameter regression estimation; data noise; flight vehicle aerodynamics; partial least square feature extraction; wavelet neural network; Aerodynamics; Educational institutions; Electronic mail; Feature extraction; Neural networks; Vehicle dynamics; Vehicles; Aerodynamic Parameters; Feature Extraction; Partial Least Square; Wavelet Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2013 32nd Chinese

Conference_Location

Xi´an

Type

conf

Filename

6639753