碩士 / 國立海洋大學 / 系統工程暨造船學系 / 90 / Abstract
Combining the real-coded evolutionary algorithm with back- propagation networks for the preliminary prediction of ship design is proposed in this thesis. There are three parts in this thesis are examined and discussed.
First part, comparing with performance of global search among Nelder- Mead’s simplex method, binary coded genetic algorithms and real- coded evolutionary algorithms through some multi-modal problems. Their results show that for the searching performance the real-coded evolutionary algorithm is the best method among them.The influences of the parameter in the real-coded evolutionary algorithm on the performance are also studied and discussed. From the studied results, the faster convergent speed and better performance could be easily achieved by using the parameters in the real-coded evolutionary algorithm such as population over 20 and replacing rate about 70% of the population. And then applying the real-code evolutionary algorithm to examine the high-dimensional barnana function, dimensions 100 ~ 200, and compares the test results with those by means of some different methods.
The second part in the thesis, three artificial neural networks such as back-propagation network, real-code evolutionary algorithm-network, and hybridized real-coded evolutionary and back-propagation neural network, are examined through nonlinear functions. Comparison of the training performances of neural network shows that the hybridized real- coded evolutionary and back-propagation neural network is the best method among them.
Finally, the hybridized real-coded evolutionary and back- propagation neural network approaches is applied to the preliminary prediction for ship design. In this thesis, 29 container ship’s basic design data are collected. The hybridized neural network approach mentioned is used neural network learning and to obtain the better efficiency that the training error for these neural network model is acceptable. Using some different testing data for the learned neural network model, the predicted data approaches to ideal data or actual data closely. In other words, the established neural network model in the thesis for the preliminary prediction for ship design could be successful.
Keyword: Nelder-Mead’s simplex method, Genetic Algorithms, Real-coded Evolutionary Algorithm, Artificial Neural network, Back-Propagation Network, Preliminary Prediction for Ship Design
| Identifer | oai:union.ndltd.org:TW/090NTOU0345008 |
| Date | January 2002 |
| Creators | hsu kai hsiung, 許凱雄 |
| Contributors | kuo hsin tsuan, 郭信川 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 115 |
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