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題 名 | Design and Analyses of Computational Neural Networks=計算類神經網路方法的設計及分析 |
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作 者 | 陳奇銘; | 書刊名 | 中國工程學刊 |
卷 期 | 22:3 1999.05[民88.05] |
頁 次 | 頁381-390 |
分類號 | 312.2 |
關鍵詞 | 計算; 類神經網路; |
語 文 | 英文(English) |
中文摘要 | 本論文,我們提出兩種用類神經網路來解決計算問題的方法。直接法,利用串列 次方展開法(power series expansions)的觀念來解決所有的計算問題。倒反法,計算函數 本身擁有一個較簡單的求解(search)類神經網路。然用運用求解計算機架構(computation- by-search scheme)可以有效解決此類複雜的計算問題。我們討論及分析此解決計算問題類 神經網路的收斂性。理論分析及模擬結果均顯示此計算類神經網路可以解決複雜的計算問題 及高次的方程式。 |
英文摘要 | In this paper, we develop two approaches for designing computation neural networks to solve computational problems. Intuitively, the direct feedforward approach, which originated from the concept of power series expansions, can solve all computational problems. Indirectly, we propose a computation-by-search (CBS) scheme, which can effectively solve some types of complicated problems, when their search functions can be easily obtained from existing neural networks. The convergence of the CBS neural networks to achieve the true solution is discussed and analyzed. Both theoretical analyses and simulated results show that the proposed neural networks can effectively solve complicated computational problems and find the real roots of higher-order polynomials. |
本系統中英文摘要資訊取自各篇刊載內容。