Jing-Rong Chang

Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly

Abstract Fault-tree analysis (FTA) is a powerful technique used to identify the root causes of undesired event in system failure by constructing a tree of sub-events, spreading into bottom events, procreating the fault and finally heading to the top event. From integrating expert’s knowledge and experience in terms of providing the possibilities of failure of bottom events, an algorithm of the intuitionistic fuzzy fault-tree analysis is proposed in this paper to calculate fault interval of system components and to find the most critical system component for the managerial decision-making based on some basic definitions. The proposed method is applied for the failure analysis problem of printed circuit board assembly (PCBA) to generate the PCBA fault-tree, fault-tree nodes, then directly compute the intuitionistic fuzzy fault-tree interval, traditional reliability, and the intuitionistic fuzzy reliability interval. The result of this proposed method is compared with the existing approaches of fault-tree methods.

Entropy-based fuzzy rough classification approach for extracting classification rules

Abstract Recently, the combination of Fuzzy Set Theory and Rough Set Theory has become a popular data mining technique for classification problems because of their strength of handling vague and imprecise data. From the previous literature, Rough Set Theory can only operate effectively with datasets containing discrete values. As most datasets contain real-valued features, it is necessary to perform a discretization step beforehand, which is typically implemented by standard fuzzification techniques. In this paper, a new fuzzification technique called Modified Minimization Entropy Principle Algorithm (MMEPA) is proposed to construct membership functions of fuzzy sets of linguistic variables. Using the dataset fuzzified by this technique to perform the rule extraction algorithm Variable Precision Rough Set Model (VP-model), the extracted classification rules by this model can obtain a higher classification accuracy rate than that of some existing methods.

A hybrid ANFIS model based on AR and volatility for TAIEX forecasting

Time series models have been applied to forecast stock index movements and make reasonably accurate predictions. There are, however, two major drawbacks of conventional time series models: (1) most conventional time series models use only one variable to forecast; and (2) the rules that are mined from artificial neural networks (ANNs) are not easily understandable. To solve these problems and enhance the forecasting performance of fuzzy time series models, this paper proposes a hybrid adaptive network-based fuzzy inference system (ANFIS) model that is based on AR and volatility to forecast stock price problems of the Taiwan stock exchange capitalization weighted stock index (TAIEX). To evaluate forecasting performance, the proposed model is compared with Chens model and Yus model. Our results indicate that the proposed model is superior to other methods with regard to root mean squared error (RMSE).

MCDM AGGREGATION MODEL USING SITUATIONAL ME-OWA AND ME-OWGA OPERATORS

OWA (Ordered Weighted Averaging) aggregation operators have been extensively adopted to handle MCDM (multiple criteria decision making) problems. However, additive or multiplicative preferences should be aggregated with feasible operators. To resolve this problem, this study proposes a new MCDM aggregation model, capable of handling situational group MCDM problems based on the ME-OWA (maximal entropy ordered weighted averaging) and ME-OWGA (maximal entropy ordered weighted geometric averaging) operators. The proposed model is also applied not only to evaluate the service quality of airlines but also select the most appropriate desalination technology. The results of previous MCDM methods can be covered with proposed model.

Extracting drug utilization knowledge using self-organizing map and rough set theory

Cardiovascular disease is becoming the major cause of death in many industrialized countries. People who receive long-term treatments usually ignore the progress of the disease states. Therefore, it is critical and necessary to evaluate drug utilization and laboratory test in order to discover the knowledge that is beneath and can be extracted from those raw data. This paper utilizes techniques of self-organizing map (SOM) and rough set theory (RST) to discover the trend of individual patients condition. With 10-fold cross-verification, the proposed SOM-SOM-RST process successfully and effectively detects patients whose diagnosis codes have been changed during the period of investigation and attains an accuracy of approximate 98%. This method can remind physicians to reevaluate the disease conditions of their patients.

A fuzzy-based military officer performance appraisal system

In this paper, we developed a computer-based group decision support system-fuzzy group decision support system (FGDSS), which include three ranking methods (intuition ranking, Lee and Lis fuzzy mean/spread and metric distance) to provide more transparent information and help manager to make better decision under fuzzy circumstance. First, we derive metric distance to rank fuzzy numbers, and we execute some simulation experiments to validate the proposed method. A practical numerical example is introduced to illustrate those three methods and compared with different alternatives by FGDSS.

Conceptual procedure for ranking fuzzy numbers based on adaptive two-dimensions dominance

Many methods for ranking of fuzzy numbers have been proposed. However, these methods just can apply to rank some types of fuzzy numbers (i.e. normal, non-normal, positive, and negative fuzzy numbers), and many ranking cases can just rank by their graphs intuitively. So, it is important to use proper methods in the right condition. In this paper, a conceptual procedure is proposed to describe how to use intuitive ranking and some technical ranking methods properly. We also introduce a new ranking fuzzy numbers approach that can adjust experts’ confidence and optimistic index of decision maker using two parameters (α and β) to handle the problems and find the best solutions. After illustrate many numerical examples following our conceptual procedure the ranking results are validity.

Flexible fuzzy OWA querying method for hemodialysis database

The hemodialysis quality contains the subjective opinions of the physicians. However, the range of good/bad quality of one physician’s perspective usually differs from the others, so we use the fuzzy theory to solve this vague situation. This paper proposes the fuzzy ordered weighting average (OWA) technique to evaluate fuzzy database queries about linguistic or precise values, which can improve the crisp values’ constrains of traditional database. Besides, we deal with the dynamical weighting problem more rationally and flexibly according to the situational parameter α value from the user’s viewpoint. In this paper, we focus on hemodialysis adequacy and develop the query system of practical hemodialysis database for a regional hospital in Taiwan. From the experimental result, we can find the overall accuracy rate is better than other methods and our result is more matching the doctor’s view. That is, the fuzzy OWA query is more flexible and more accurate

Evaluating the airline service quality by fuzzy OWA operators

The OWA (Ordered Weighted Averaging) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators (including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model – dynamic fuzzy OWA operators based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a “magnifying lens” to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. We also apply proposed model to evaluate the service quality of airline.

Cardinality-based fuzzy time series for forecasting enrollments

Forecasting activities are frequent and widespread in our life. Since Song and Chissom proposed the fuzzy time series in 1993, many previous studies have proposed variant fuzzy time series models to deal with uncertain and vague data. A drawback of these models is that they do not consider appropriately the weights of fuzzy relations. This paper proposes a new method to build weighted fuzzy rules by computing cardinality of each fuzzy relation to solve above problems. The proposed method is able to build the weighted fuzzy rules based on concept of large itemsets of Apriori. The yearly data on enrollments at the University of Alabama are adopted to verify and evaluate the performance of the proposed method. The forecasting accuracies of the proposed method are better than other methods.