On the construction of wavelet network
91 p.
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2011
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sg-ntu-dr.10356-474802023-03-04T00:45:03Z On the construction of wavelet network Chen, Fei Shi Daming School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence 91 p. 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. MASTER OF ENGINEERING (SCE) 2011-12-27T08:25:36Z 2011-12-27T08:25:36Z 2006 2006 Thesis Chen, F. (2006). On the construction of wavelet network. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/47480 10.32657/10356/47480 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Chen, Fei On the construction of wavelet network |
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91 p. |
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Shi Daming |
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Shi Daming Chen, Fei |
format |
Theses and Dissertations |
author |
Chen, Fei |
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Chen, Fei |
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On the construction of wavelet network |
title_short |
On the construction of wavelet network |
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On the construction of wavelet network |
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On the construction of wavelet network |
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On the construction of wavelet network |
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on the construction of wavelet network |
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2011 |
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https://hdl.handle.net/10356/47480 |
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1759855184484237312 |