Please use this identifier to cite or link to this item:
Title: On the construction of wavelet network
Authors: Chen, Fei
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2006
Source: Chen, F. (2006). On the construction of wavelet network. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: By taking advantage of both the scaling properties of wavelets and the high learning ability of neural networks, a wavelet network exhibits a high approximation and prediction capability. It can well approximate data, and thus be a good mathematic tool to find the hidden nature of a sequence of data, and to visualize the behavior of the data. According to Ocam's Razor hypothesis, compact models are believed to have the best generalization ability, since they represent data with the lowest structural complexity. Therefore, this research aims to construct a compact wavelet network, which can achieve an ideal approximation performance with the least number of wavelets. There are three steps involved to construct a wavelet network. First, a wavelet candidate pool is built up with a pyramid type initialization method. After that, the orthogonal least square (OLS) algorithm is employed to find those most critical wavelets. Finally, a stochastic gradient algorithm is utilized to recursively adjust the parameters.
Description: 91 p.
DOI: 10.32657/10356/47480
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

Files in This Item:
File Description SizeFormat 
SCE_THESES_22.pdf8.89 MBAdobe PDFThumbnail

Page view(s) 50

Updated on May 9, 2021

Download(s) 10

Updated on May 9, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.