三角模糊數常被用來表現模糊的概念。使用三角模糊數的優點包括了計算的簡 化、值的直覺性、滿足0損失之重建要求等等。本研究擴充原本三角模糊數之定義，並提出 兩種新的類三角模糊數─廣義三角模糊數（Generalized TFNs）與截角三角模糊數 （Truncated TFNs）。同時亦導出其分別之算術運算規則。這些新的類三角模糊數可與三角模糊數結合， 用於原本為三角模糊數驅動的系統當中，以提供更高的精確度。另外，這些類三角模糊數 驅動的系統當中，模糊參數的估計方法亦加以討論，包括ACT、TFNA、GTFNA和PF四種， 其優缺點於最後加以歸納。

　　　　　Triangular Fuzzy Number (TFN) is frequently used to represent fuzzy concepts in many applications. The advantages of applying TFNs include the simplicity of associative calculations, instinct interpretation of values, satisfaction of error-free reconstruction, and so on. In the first part of this study, two TFN-like LR-type fuzzy numbers, including the Truncated Triangular Fuzzy Number (TTFN) and the Generalized Triangular Fuzzy Number (GTFN) are introduced. Next, the concept of a TFN-driven system is defined, and the approximation of fuzzy parameters in such a system is investigated. Four approaches including Alpha Cut Tabling (ACT), Triangular Fuzzy Number Approximation (TFNA), Generalized Triangular Fuzzy Number Approximation (GTFNA), and Polynomial Fitting (PF) are proposed for achieving this purpose. Demonstrative examples are given. Advantages and disadvantages of each method are also discussed.