DocumentCode
1595597
Title
Feedforward neural network´s denoising with wavelet basis
Author
Jianwei, Li ; Chengge, Zhong ; Huachun, Dong ; Taifan, Quan
Author_Institution
Dept. of Electron. & Commun. Eng., Harbin Inst. of Technol., China
Volume
2
fYear
1996
Firstpage
1373
Abstract
Methods based on wavelet transform theory for decreasing sampling noise in feedforward neural networks are proposed in this paper. Wavelet bases are employed in the network to constrain the network´s ability in learning samples which are corrupted by noise. The selection of the wavelet bases which correlate with the map to be approximated is mainly discussed
Keywords
correlation methods; feedforward neural nets; interference suppression; learning (artificial intelligence); noise; signal sampling; wavelet transforms; feedforward neural networks; learning samples; sampling noise; wavelet bases; wavelet basis; wavelet transform theory; Computational Intelligence Society; Feedforward neural networks; Neural networks; Noise reduction; Quantization; Quantum mechanics; Sampling methods; Signal mapping; Signal processing; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing, 1996., 3rd International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-2912-0
Type
conf
DOI
10.1109/ICSIGP.1996.566565
Filename
566565
Link To Document