碩士 / 國立交通大學 / 土木工程系 / 89 / That tidal levels obtained from tidal theory are added as inputs in artificial neural network model is found to improve prediction ability for tidal levels in this paper. The optimum structure of the present artificial neural network model for each station is set up from examining the learning rate, moment factor, input parameters, numbers of hidden layer, learning times and input length. The optimum ANN models for three kinds of tidal types also have five inputs that are two observed tidal levels and three theoretical tidal levels and have learning rate of 0.1 and moment factor of 0.8, respectively. The optimum model for semi-diurnal type at Hsian-Chu station is I5H6O1 with 500 learning times. The optimum model for both mixed type at Hou-pi-hu station and full diurnal type at Pi-tou-chiau station is I5H12O1. The observed tidal data have seasonal deviation from mean water level because of temperature and are deseasonalized by moving Gaussian average with a length of 360 hours. The ANN models have better long-term forecasting for deseasonalized tidal data.
| Identifer | oai:union.ndltd.org:TW/089NCTU0015019 |
| Date | January 2001 |
| Creators | Yan-Jer Tseng, 曾彥 |
| Contributors | 張憲國 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 94 |
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