標題: | 動態誤差反向傳遞學習法應用於系統識別與控制 Dynamic Back-Propagation for Plant Identification and Control |
作者: | 王南景 Nan-Ching Wang 周志成 Dr. Chi-Cheng Jou 電控工程研究所 |
關鍵字: | 關鍵詞:類神經網路;最佳化;系統識別;控制;Neural network; optimization; plant identification; control |
公開日期: | 1993 |
摘要: | 雖然目前類神經網路的研究多著重於靜態的前饋式網路上,但未來應用動 態網路於控制相關的問題將愈加重要。所謂的動態網路有二種可能情況: 一是具回饋連接的回歸式網路,另一是動態系統中包含前饋式網路。本論 文重點在探討動態網路的學習法則,稱為動態誤差反向傳遞學習法。主要 的研究內容有:(1) 回歸式與前饋式網路於控制相關應用方法的歸納與分 類;(2) 利用動態最佳化方法推導多種動態誤差反向傳遞學習法則;(3) 應用回歸式與前饋式網路於系統識別的方法和技術; (4) 應用回歸式與 前饋式網路於調節與追蹤的方法和技術;(5) 實證比比較回歸式與前饋式 網路應用於非線性動態系統的效果。 While much of the recent emphasis in the connectionist reseaarch has been on feedforward networks with static back- propagation, it is likely that the use of dynamic networks will be of particular importance in control-related applications. This thesis is focused on a learning methodology for recurrent networks with feedback connections and feedforward networks as subsystems in a dynamic system. Such a learning methodology is termed dynamic back-propagation, which is one of the most prominent learning methods for connectionist networks. A detailed study of dynamic back-propagation is presented to provide an insight of the principal ideas that contributed to the evolution of the concept and the details concerning its practical applications to identification and control. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#NT820327020 http://hdl.handle.net/11536/57735 |
顯示於類別: | 畢業論文 |