不同於以往大多數針對確定性資料的學習方法，自模糊集合理論發表後，使得如人類語意之不確性資料獲得學習的可能。模糊推論系統(FIS)基於此開發使得不確定性資料獲得系統性的學習，然其需藉由調整參數組的方式以獲取最佳學習結果，而此參數組包含前部件以及後部件，其中前部件參數包含輸入屬性之隸屬函數(MF)數量、以及描述其形狀與值域的參數值，而後部件參數則為依據前部件之修訂結果所建構之線性模式係數。Jang則於1993年提出結合類神經網路(ANN)之疊代學習法修訂FIS之參數組，稱為適應性類神經模糊推論系統(ANFIS)。雖ANFIS可藉由ANN取得最優之前部件參數而獲得最優結果，然初始前部件參數之MF設定值亦會影響其最終結果。此外，在分類問題上，MF之個數可依據輸出屬性之可能類別值數量決定，然在數值預測問題上，卻無法確切決定此數量。因此本研究提出一個系統性的流程以決定此初始前部件的參數設定，首先使用模糊C-means分群法搭配模糊側影係數決定最適分群數為MF數量，再使用整體趨勢擴散技術對此時樣本分群結果進行模糊MF之參數值推估。實驗階段，本研究藉由從面板產業取得之實務資料進行測試，其結果顯示確能有效改善ANFIS對於數值預測問題之預測準確度。

The Adaptive Network-Based Fuzzy Inference System (ANFIS) is widely applied to classification and numerical forecasting problems nowadays. Although the ANFIS is developed to obtain the optimal results of FIS with artificial neural networks by adapting the initial antecedent parameters in FIS, the profiles of fuzzy membership functions, how to well set the initial antecedent parameters still can affect the learning results. Focusing on the numerical forecasting problems, this study develops a systematic procedure, which employs the Fuzzy C-means with the fuzzy silhouette coefficients to identify the locations of observations with the most suitable clustering sizes, and then based on which the mega-trend-diffusion technique is taken to estimate the membership functions as the initial antecedent parameters for ANFIS. In the experiment, a real case taken from one of the leading TFT-LCD (thin-film transistor liquid-crystal displays) manufacturers is examined. The experiment results reveal that the predictive preciseness of ANFIS is improved when the initial antecedent parameters are set with the proposed procedure.

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