在熔融紡絲製造過程中，紗線均勻度影響紗的外觀、毛羽、強度與產率等性質，進而影響產品生產、利潤與產品棄置不符使用等問題，紗線變異大亦造成紗線的瑕疵，而變異小的紗線可產生較穩定的品質。本研究應用模糊類神經網路控制理論於熔融紡絲機台，藉捲取羅拉轉速的改變調整紗線直徑，以控制紗線直徑平均值於目標值及降低紗線直徑之變異。本文以直徑誤差與直徑誤差變化量為輸入，捲取羅拉轉速增加量為輸出，經模糊類神經網路的學習來調整第二層歸屬函數的中心點與標準偏差值和調整第三至四層的連結權重值，以達到收斂和學習之效果，經由模糊類神經網路控制器能維持紗線直徑平均值及降低紗線直徑的變異，印證所提之方法可成功地應用在紗線均勻度之線上控制。

In melt spinning, the yarn evenness plays a critical role in appearance, hairiness, strength, and productivity of yarn, and further affects its production, profit, disposal of the unqualified products. Larger variance of yarn also causes defects of yarn, while yarn with smaller variance will yield more uniform quality. In this study, fuzzy neural network controllers maintain the mean value of yarn diameter at the desired value and reduce the yarn diameter variance. The control law is implemented in a laboratory scale of the melt spinning setup and the take-up roll speed is adjustable to regulate the yarn diameter. The diameter error and its variation are the inputs and the increased amount take-up roll speed is the output. The learning of fuzzy neural network adjusts the center and standard deviation of membership functions in the second layer and link weight values of the third to fourth layer to achieve the effect of convergence and learning. The fuzzy neural network controller can maintain the mean value of yarn diameter at the desired value and lower the variance of yarn diameter. The proposed approaches has been carried out successfully in on-line control of the yarn evenness.

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