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動態徑向基底函數網路與混沌預測 / Dynamical Radial Basis Function Networks and Chaotic Forecasting

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.

Identiferoai:union.ndltd.org:CHENGCHI/B2002004242
Creators蔡炎龍, Tsai, Yen Lung
Publisher國立政治大學
Source SetsNational Chengchi University Libraries
Language英文
Detected LanguageEnglish
Typetext
RightsCopyright © nccu library on behalf of the copyright holders

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