Title :
An improved adaptive algorithm for wavelet transform and its application
Author :
GuiLin Lu ; Wang, ShaoHong
Author_Institution :
Guangxi Univ. of Technol., Liuzhou, China
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;
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
DOI :
10.1109/ICIME.2010.5477539
