An adaptive learning algorithm for a wavelet neural network

被引：15

作者：

Bodyanskiy, Y ^{[1
]}

Lamonova, N ^{[1
]}

Pliss, I ^{[1
]}

Vynokurova, O ^{[1
]}

机构：

[1] Kharkiv Natl Univ Radio Elect, Control Syst Res Lab, UA-61166 Kharkov, Ukraine

来源：

EXPERT SYSTEMS | 2005年 / 22卷 / 05期

关键词：

prediction; emulation; non-stationary noisy signals; hybrid wavelet neural network; online learning algorithm;

D O I：

10.1111/j.1468-0394.2005.00314.x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm provides not only the tuning of synaptic weights in real time, but also the tuning of dilation and translation factors of daughter wavelets. The algorithm has both tracking and smoothing properties, so the wavelet networks trained with this algorithm can be efficiently used for prediction, filtering, compression and classification of various non-stationary noisy signals.

引用

页码：235 / 240

页数：6

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