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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
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
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,
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
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.
Appears in Collections:機械工程學系所

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