An improved adaptive algorithm for wavelet transform and its application

Author

GuiLin Lu ; Wang, ShaoHong

Author_Institution

Guangxi Univ. of Technol., Liuzhou, China

fYear

2010

fDate

16-18 April 2010

Firstpage

537

Lastpage

541

Abstract

An improved adaptive algorithm for wavelet transform based on lifting schemes is proposed, cited to prove determine for Iterative weight of LMS, Applies to the subtle characteristics of the signal recognition, This is the desired signal, noise and interference signals to identify the classification. Experimental results show that: The genetic optimization algorithm weight update iteration by through the RBF neural network, sub-band Alter are synthesis and decomposition effective. Determine the DOA, Enhance the desired signal power, Effective removal of noise, Signal can be determined, Reconstruction of the signal is more closer to the original signal, Verify the correctness of the adaptive wavelet algorithm.

Keywords

genetic algorithms; interference suppression; iterative methods; radial basis function networks; signal reconstruction; wavelet transforms; DOA; LMS; RBF neural network; adaptive wavelet transform; genetic optimization algorithm; signal interference; signal recognition; signal reconstruction; weight update iteration; Adaptive algorithm; Character recognition; Genetics; Interference; Iterative algorithms; Least squares approximation; Network synthesis; Neural networks; Signal processing; Wavelet transforms; Adaptive; Genetic Algorithm; RBF Network Optimizes; Transform; wavelet;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-5263-7

Electronic_ISBN

978-1-4244-5265-1

Type

conf

DOI

10.1109/ICIME.2010.5477539

Filename

5477539