| Summary: | 碩士 === 國立臺灣科技大學 === 資訊工程系 === 95 === In this thesis, we present two methods for fuzzy risk analysis based on ranking generalized fuzzy numbers and similarity measures between interval-valued fuzzy numbers. First, we present a new method for ranking generalized fuzzy numbers for handling fuzzy risk analysis problems. The proposed method considers defuzzified values, the weight and the spreads of generalized fuzzy numbers. Moreover, we also apply the proposed method for ranking generalized fuzzy numbers to present a new method for dealing with fuzzy risk analysis problems. Then, we present a new similarity measure for interval-valued fuzzy numbers. The proposed similarity measure considers five factors, i.e., the degree of similarity on X-axis between the upper fuzzy numbers of the interval-valued fuzzy numbers, the degree of similarity about the weight of the upper fuzzy numbers of the interval-valued fuzzy numbers, the spread between the upper fuzzy numbers of the interval-valued fuzzy numbers, the degree of similarity on the X-axis between the interval-valued fuzzy numbers, and the degree of similarity on the Y-axis between the interval-valued fuzzy numbers. Moreover, we also present new interval-valued fuzzy numbers arithmetic operators and apply the proposed similarity measure to present a new method for dealing with fuzzy risk analysis problems based on interval-valued fuzzy numbers. The proposed fuzzy risk analysis methods provide us a useful way for handling fuzzy risk analysis problems.
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