在稀疏模糊規則庫系統中，模糊規則庫通常不完整，在這種情形下，系統可能無法完整執行模糊推理以取得合理的推論結果。為了克服此缺點，我們需要發展在稀疏模糊規則系統中作模糊內插推理之技術。在本論文中，我們提出一個新的模糊內插推理方法，主要使用α切割(α-cut)與轉換的技術，其可以產生比目前已存在的方法更合理的推論結果。另外，我們也延伸了α切割與轉換的技術提出一個新方法以處理在稀疏模糊規則系統中作加權模糊內插推理之新方法，對於模糊規則具多前提變數之模糊內插推理，其允許模糊規則的前置部份考慮權重因素。本論文所提的方法可以很有用的在模糊規則庫系統中作模糊內插推理。

In sparse fuzzy rule-based systems, the fuzzy rule bases usually are incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpola¬tive reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. Moreover, we also extend the α-cuts and transformation techniques to present a new method to handle the weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. For multiple antecedent variables fuzzy rules interpolation, the proposed method allows each linguistic variable appearing in the antecedent parts of fuzzy rules associated with a weighting factor. The proposed methods provide a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.

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