Wavelet Transform Applied to Fractal, Image Coding and Neural Networks to Identify Chaotic Systems
| Autor: | Yen-Ping Chou, 周延平 |
|---|---|
| Rok vydání: | 1994 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 82 The wavelet transform (WT) provides an analyzing tool to decompose a signal into several components in a flexible time- frequency plane. The discrete wavelet transform (DWT), particularly combined orthonormal finite supported wavelets with multiresolution analysis, has been regarded as an efficient and easy-implemented WT. In this thesis, we first investigate the continuous WT and present its application of fractals. Then, based on the research of Daubechies, we explore another spectral factorization to establish some compactly supported wavelets for the DWT. Next, we apply the DWT to two physical cases. Using the extended version of the DWT$-$two- dimensional DWT, we convert an image into several wavelet coefficients. Then these subimages are quantized and encoded in order to perform compression. In the second application, we propose a wavelet-based neural network (WBNN) to identify chaotic systems. The WBNN structure overcomes a disadvantage of time-consuming, existing in the training process of conventional neural networks. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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