在許多的研究和應用之中都需要預測的技巧。本論文中, 我們建構了一個
新的神經網路模式--動態徑向基底函數 (dynamical radial basis
function) 網路 (DRBF網路) , 並且用這種模式的神經網路作為「函數近
似子」(function approximator) 去處理預測上的問題。另外我們也設計
幾種不同的學習演算法以測試DRBF網路的功能。 / The forecasting technique is important for many researches and
applications. In this paper, we shall construct a new model of
neural networks -- the dynamical radial basis function (DRBF)
networks and use the DRBF networks as "function approximators"
to solve some forecasting problems. Different learning
algorithms are used to test the capability of DRBF networks.
| Identifer | oai:union.ndltd.org:CHENGCHI/B2002004242 |
| Creators | 蔡炎龍, Tsai, Yen Lung |
| Publisher | 國立政治大學 |
| Source Sets | National Chengchi University Libraries |
| Language | 英文 |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
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