在本論文中，我們考慮遞迴式區間型 type-2 模糊系統，其中規則後鑑部包含遞迴 架構。我們提出一個混合式學習演算法來建構此模糊系統。此混合式學習演算法主要 包含自我建構規則產生法及基於遞迴式奇異值分解之最小平方估計值。透過自我建構 規則產生法先將資料分群，在建立了初始化的規則架構之後，再利用遞迴式區間型 type-2 TSK 模糊法則搭配基於遞迴式奇異值分解之最小平方估計值來對參數做修正。
在架構學習的部分，利用自我建構規則產生法的優點是可以先將資料分割成數個群集，
且在新增資料時不需要重新決定群集數目；接著在參數學習的部分，我們利用了遞迴 式區間型 type-2 TSK 模糊法則搭配基於遞迴式奇異值分解之最小平方估計值，其優點 是利用遞迴式區間型 type-2 TSK 模糊規則，對於處理時間序列上的資料來說，可以減 少群集的數目，且利用了基於遞迴式奇異值分解之最小平方估計值，更可以改善原本 奇異值分解法在處理龐大矩陣上的缺點。而在實驗當中，也遇到了一些本方法在執行 上可能產生的問題，這些問題將是我們日後可以加以改善修正的方向。未來考慮可以 修正的方向如下:
對於雜訊點的干擾使準確度下降及群集數目增加。
改善對於決定初始群集中心位移值時較缺乏彈性。
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