Shyi-Ming Chen

Handling multicriteria fuzzy decision-making problems based on vague set theory

Abstract New techniques for handling multicriteria fuzzy decision-making problems based on vague set theory are presented. The proposed techniques allow the degrees of satisfiability and non-satisfiability of each alternative with respect to a set of criteria to be presented by vague values. Furthermore, the proposed techniques allow the decision-maker to assign a different degree of importance to each criteria. The techniques proposed in this paper can provide a useful way to efficiently help the decision-maker to make his decisions.

Forecasting enrollments based on fuzzy time series

Abstract This paper presents a new method to forecast university enrollments based on fuzzy time series. The data of historical enrollments of the University of Alabama shown in Song and Chissom (1993a, 1994) are adopted to illustrate the forecasting process of the proposed method. The robustness of the proposed method is also tested. The proposed method not only can make good forecasts of the university enrollments, but also can make robust forecasts when the historical data are not accurate. The proposed method is more efficient than the one presented in Song and Chissom (1993a) due to the fact that the proposed method uses simplified arithmetic operations rather than the complicated max-min composition operations presented in Song and Chissom (1993a).

Knowledge representation using fuzzy Petri nets

A fuzzy Petri net model (FPN) is presented to represent the fuzzy production rule of a rule-based system in which a fuzzy production rule describes the fuzzy relation between two propositions. Based on the fuzzy Petri net model, an efficient algorithm is proposed to perform fuzzy reasoning automatically. It can determine whether an antecedent-consequence relationship exists from proposition d/sub s/ to proposition d/sub j/, where d/sub s/ not=d/sub j/. If the degree of truth of proposition d/sub s/ is given, then the degrees of truth of proposition d/sub j/ can be evaluated. The formal description of the model and the fuzzy reasoning algorithm are shown in detail. The upper bound of the time complexity of the fuzzy reasoning algorithm is O(nm), where n is the number of places and m is the number of transitions. Its execution time is proportional to the number of nodes in a sprouting tree generated by the algorithm only generates necessary reasoning paths from a starting place to a goal place, it can be executed very efficiently. >

FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES

A drawback of existing fuzzy forecasting methods based on fuzzy time series is that they use the first-order fuzzy time series to deal with forecasting problems in which the forecasting results are not good enough. Using a high-order fuzzy time series to deal with fuzzy forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the high-order fuzzy time series model to deal with forecasting problems. Based on the proposed model, we develop an algorithm to forecast the enrollments of the University of Alabama, where the historical enrollment data at the University of Alabama (Song and Chissom 1993a, 1994) are used to illustrate the forecasting process. The forecasting accuracy of the proposed method is better than that of the existing methods.

Temperature prediction using fuzzy time series

A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time series model to deal with forecasting problems. Based on the proposed model, we develop two algorithms for temperature prediction. Both algorithms have the advantage of obtaining good forecasting results.

Handling forecasting problems using fuzzy time series

Abstract In [6–9], Song et al. proposed fuzzy time-series models to deal with forecasting problems. In [10], Sullivan and Woodall reviewed the first-order time-invariant fuzzy time series model and the first-order time-variant model proposed by Song and Chissom [6–8], where the models are compared with each other and with a time-invariant Markov model using linguistic labels with probability distributions. In this paper, we propose a new method to forecast university enrollments, where the historical enrollments of the University of Alabama shown in [7,8] are used to illustrate the forecasting process. The average forecasting errors and the time complexity of these methods are compared. The proposed method is more efficient than the ones presented in [7, 8, 10] due to the fact that the proposed method simplifies the arithmetic operation process. Furthermore, the average forecasting error of the proposed method is smaller than the ones presented in [2, 7, 8].

Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method

Type-2 fuzzy sets involve more uncertainties than type-1 fuzzy sets. They provide us with additional degrees of freedom to represent the uncertainty and the fuzziness of the real world. In this paper, we present an interval type-2 fuzzy TOPSIS method to handle fuzzy multiple attributes group decision-making problems based on interval type-2 fuzzy sets. We also use some examples to illustrate the fuzzy multiple attributes group decision-making process of the proposed method. The proposed method provides us with 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.

Measures of similarity between vague sets

In this paper, we propose two similarity measures for measuring the degree of similarity between vague sets. The proposed measures can provide a useful way for measuring the degree of similarity between vague sets.

A new approach to handling fuzzy decision-making problems

Techniques for handling fuzzy decision-making problems are presented in which fuzzy production rules and fuzzy set theory are used for knowledge representation. The maximum fuzzy cover generation techniques used are described in detail. Some examples are given to illustrate the maximum fuzzy cover generation process. The examples are restricted to medical diagnostic problems; however, the techniques can be applied to any other decision-making problem.<<ETX>>

Fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers

In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7].