S
Shyi-Ming Chen
Researcher at National Taiwan University of Science and Technology
Publications - 433
Citations - 25371
Shyi-Ming Chen is an academic researcher from National Taiwan University of Science and Technology. The author has contributed to research in topics: Fuzzy set operations & Fuzzy number. The author has an hindex of 90, co-authored 425 publications receiving 22172 citations. Previous affiliations of Shyi-Ming Chen include National Taiwan University & Jinwen University of Science and Technology.
Papers
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Handling multicriteria fuzzy decision-making problems based on vague set theory
Shyi-Ming Chen,Jiann-Mean Tan +1 more
TL;DR: New techniques for handling multicriteria fuzzy decision-making problems based on vague set theory can provide a useful way to efficiently help the decision-maker to make his decisions.
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Forecasting enrollments based on fuzzy time series
TL;DR: A new method to forecast university enrollments based on fuzzy time series based on simplified arithmetic operations rather than the complicated max-min composition operations presented in Song and Chissom (1993a).
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Knowledge representation using fuzzy Petri nets
TL;DR: A fuzzy Petri net model (FPN) is presented to represent the fuzzy production rule of a rule-based system in which a fuzzy productionrule describes the fuzzy relation between two propositions and an efficient algorithm is proposed to perform fuzzy reasoning automatically.
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Forecasting enrollments based on high-order fuzzy time series
TL;DR: The proposed high-order fuzzy time series model is developed and an algorithm to forecast the enrollments of the University of Alabama is developed, where the historical enrollment data at theUniversity of Alabama are used to illustrate the forecasting process.
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Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method
Shyi-Ming Chen,Li-Wei Lee +1 more
TL;DR: The proposed method provides a useful way to handle fuzzy multiple attributes group decision-making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of the attributes.