Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/1772
標題: 二階段式最佳陡降學習法則及其在塑膠射出成型機料管溫度控制之應用
Neural Network Cascade Steepest Descendant Learning Algorithm with application on Precise Temperature Control Control of Injetion Molding Barrel System
作者: 陳在鈞
Chen, Tsay-Juin
關鍵字: 類神經網路;參數自調;射出成型機;料管;PID參數自調;Neural Network;Self-tuning;Injection Molding Mechine;Barrel;PID Self-tuning
出版社: 機械工程學系
摘要: 
"類神經網路"以其良好之非線性映射能力,己經被廣泛的應用於系統模式
建立、模糊隸屬函數調整、文字辨識及複雜之非線性系統控制 ?等方面
。本論文主要目的乃是希望將類神經網路應用於極非線性之射出成型機料
管系統鑑別與精密溫度控制上。為了對料管系統做更完整之鑑別,本研究
嘗試在類神經網路EBP(誤差逆傳遞學習法則)架構之下,由一階逼進著手
,在簡單的原則下,以最陡負梯度法則及參數上下限設定的方式,針對每
一次權值更新,找出增進模式或批次模式相對應之最佳學習參數 及學習
慣量 參數值,使得網路能在最少的次數下收斂,且能有不錯的重現性,
電腦模擬證明本研究所提出之新法則在收斂速度與學習成功率上,皆較其
他方法優異。在本研究之第二部分,我們先將所發展之新類神經網路學習
法則應用於射出成型機料管系統之模擬上,實際實驗結果證明,本研究所
提出之新方法確實可對複雜之料管系統做有效的鑑別。完成之類神經網路
料管模式則進一步用來做為類神經網路式參數自調PID控制器之依據,實
際實驗結果亦證明此參數自調PID控制器能將料管溫度控制於 +0.5~-0.5
度內
Artificial neural networks, with its high learning and nonlinear
mapping ability, have been successfully applied to many system
identification and control problems. The goal of this thesis is
to apply the neural network techniques to the system
identification and precise temperature control of the extremely
nonlinear injection molding barrel system.In order to complete
the system identification work as accuracy as possible, we
propose a new and efficient multilayer neural network learning
algorithm first. In this new learning algorithm (Cascade
steepest descendant learning algorithm) the steepest descendant
method is used to search the optimal learning constant ηand
momentum term αfor each weights updating process. The well
known Delta learning rule is then employed to modify the
connecting weights in terms of the optimal ηand α. Computer
simulations show that the proposed new algorithm outmatches
other learning algorithms both in converging speed and success
rate.In the second part of this research, we first apply the new
neural network learning algorithm to the identification of the
injection molding barrel system. Real experiment results
demonstrate that the new algorithm can precisely identify the
complicate barrel system. Further more, a self-tuning PID
controller based on the trained neural network barrel model for
precise temperature control is developed. Real experiments show
that the proposed self-tuning PID controller can precisely
control the barrel temperature within 0.5 degree.
URI: http://hdl.handle.net/11455/1772
Appears in Collections:機械工程學系所

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