An Algorithm of Wavelet Network Learning from Noisy Data

Author

Zhang, Zhiguo ; San, Ye

Author_Institution

Control & Simulation Center, Harbin Inst. of Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

2746

Lastpage

2751

Abstract

Noise often leads to bad generalization of network. Many of the typical algorithms can not be applied for the on-line identification of complex system since they are not robust to the variance of the energy of noise. A new algorithm is proposed to solve this problem based on the frequency band of wavelet network. It is shown that the wavelet network trained by the new algorithm is a low-pass filter, which has removed the noise out the frequency band of network. Since the frequency band of noise is usually higher than that of network, the variance of noise little influences the identification of wavelet network, so the new algorithm is robust to the variance of noise. The analysis of theory and the results of simulation show that the new algorithm has the capacity of avoidance of overfitting and the robustness of noise

Keywords

generalisation (artificial intelligence); learning (artificial intelligence); low-pass filters; neural nets; wavelet transforms; complex system; convergence; generalization; low-pass filter; noise removal; noisy data; overfitting; robustness; wavelet network identification; wavelet network learning; Algorithm design and analysis; Analytical models; Automation; Electronic mail; Frequency; Intelligent control; Low pass filters; Noise robustness; convergence algorithm; overfitting; removal of noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712864

Filename

1712864