Tian-Wei Sheu

Multicriteria fuzzy decision making based on interval-valued intuitionistic fuzzy sets

In this paper, we present a new method for multicriteria fuzzy decision making based on interval-valued intuitionistic fuzzy sets, where interval-valued intuitionistic fuzzy values are used to represent evaluating values of the decision-maker with respect to alternatives. First, we propose a new method for ranking interval-valued intuitionistic fuzzy values. Based on the proposed fuzzy ranking method of interval-valued intuitionistic fuzzy values, we propose a new method for multicriteria fuzzy decision making. The proposed multicriteria fuzzy decision making method outperforms Yes method (2009) due to the fact that the proposed method can overcome the drawback of Yes method (2009), where the drawback of Yes method is that it can not distinguish the ranking order between alternatives in some situations. The proposed method provides us with a useful way for dealing with multicriteria fuzzy decision making problems based on interval-valued intuitionistic fuzzy sets.

TAIEX Forecasting Using Fuzzy Time Series and Automatically Generated Weights of Multiple Factors

In this paper, we present a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) using fuzzy time series and automatically generated weights of multiple factors. The proposed method uses the variation magnitudes of adjacent historical data to generate fuzzy variation groups of the main factor (i.e., the TAIEX) and the elementary secondary factors (i.e., the Dow Jones, the NASDAQ and the M1B), respectively. Based on the variation magnitudes of the main factor TAIEX and the elementary secondary factors of a particular trading day, it constructs the occurrence vector of the main factor and the occurrence vectors of the elementary secondary factors on the trading day, respectively. By calculating the correlation coefficients between the numerical data series of the main factor and the numerical data series of each elementary secondary factor, respectively, it calculates the relevance degree between the forecasted variation of the main factor and the forecasted variation of each elementary secondary factor. Based on the correlation coefficients between the numerical data series of the main factor and the numerical data series of each elementary secondary factor on a trading day, it automatically generates the weights of the occurrence vector of the main factor and the occurrence vector of each elementary secondary factor on the trading day, respectively. Then, it calculates the forecasted variation of the main factor and the forecasted variation of each elementary secondary factor on the trading day, respectively, to obtain the final forecasted variation on the trading day. Finally, based on the closing index of the TAIEX on the trading day and the final forecasted variation on the trading day, it generates the forecasted value of the next trading day. The experimental results show that the proposed method outperforms the existing methods.

A new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques

This paper presents a new method for fuzzy forecasting based on two-factors high-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. We fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors high-order fuzzy logical relationships. Then, we group the two-factors high-order fuzzy logical relationships into two-factors high-order fuzzy-trend logical relationship groups. Finally, we obtain the optimal weighting vectors for each fuzzy-trend logical relationship group by using particle swarm optimization techniques to perform the forecasting. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

Using the Combination of GM(1,1) and Taylor Approximation Method to Predict the Academic Achievement of Student

The purpose of this study is to predict the academic achievement of student based on the combination of GM(1,1) and Taylor approximation method(abbreviated as T-GM(1,1)). The prediction model combined the first-order two variables grey differential equation model from grey system theory and Taylor approximation method from approximation optimization theory. This combined model can obtain the most optimal predicted value by multi-times approximate calculation. In addition, the researchers used MATLAB software to build a MATLAB toolbox for the prediction model based on GM(1,1) and T-GM(1,1). The experimental results showed that T-GM(1,1) can be adjusted repeatedly until reaches the optimal values and makes the predicted error reduce to the minimum. The comparison of the obtained results with the original GM(1,1) showed that T-GM(1,1) is a good alternative for parameters optimization of this prediction model.

RGSP Toolbox 1.0 for Educational Achievement

The main purpose of this research is to develop a toolbox for GSP chart and Rasch GSP methods. Through GSP chart analysis, teachers get information to diagnose the learning status of students and the quality of items. Rasch GSP provides information to assess the quality of the classes and test quality. The RGSP Toolbox 1.0 developed by this study is not a toolbox that implements calculation according to the basic steps of the two above methods. In order to increase the application range of the toolbox, this study has also proposed and executed a number of improvements. So, the RGSP Toolbox 1.0 is a useful tool to support teachers and researchers in educational achievement. This toolbox can quickly calculate and give exact results, draw graphs clearly. In addition, a graphical user interface (GUI) is also designed to be easy for use. The toolbox not only supplies researchers with big support in their research process but also can provide feedbacks about teaching and learning for teachers and students. So students can make reasonable adjustments for learning activity and teachers have an effective reference for guidance of learning.

Study on the Conception of Learning Problems of Students by Combining the Misconception Domain and Structural Analysis Methods

The purpose of this paper was to analyze the structure of difficult concepts learning within the classroom. The sample of the study was 18 fourth grade students in Central Taiwan, and the exam tools were produced by teachers for math exams. In this paper, a combination of the Rasch Model GSP chart analysis theory, the misconception domain, the Interpretive Structural Modeling (ISM) and Grey Structural Model (GSM) was used. The results are as follows: (1) Based on the change in students’ average grade and the difficulty between problems of Rasch Line = 0.5, the effect of the remedial teaching will be known. (2) Through the GSM structural graphs, the misconception domain structure of students can be identified. (3) The problem-concept relationship of misconception domain can reveal the structure of misconceptions through the GSM structural graph. This structure is capable of identifying the learning sequence of the difficult concepts. (4) Upon comparing the GSM structural graph of misconception domain before and after remedial teaching, it became clear which concepts made learning more effective, and which concepts needed to be developed more. (5) The research method for a small amount of people and problems can still systematic point out the structure of concepts which need in this class.

Educational evaluation identification and structural analysis on industrial-design product modeling course

The course of product model-making is to train students to be able to design three-dimensional creations, and acquire the knowledge and skill in the modeling field. The purpose of this paper is to evaluate the professional learning items, and the results can be applied to adjust the teaching content and improve the teaching methods. The paper chose the students graduated from the Department of Industrial Design, and are working in the product modeling design field as examinees. The data were analyzed by Grey Relational Analysis (GRA) to evaluate the difficulties of the course content and also put them in order, and the Student-Problem Chart (S-P Chart) was used to establish the Grey Student-Problem Chart (GSP Chart), which provides the cognitive domain of the examinees and becomes the basis of the paper. At the end, the results are shown in the GSM (Grey Structural Modeling) which become the analytical basis of this paper. Through this objective evaluation, the figures and data generated from the paper can clarify the course evaluation which becomes the best Innovative evaluation methods in educational learning and training.

The Study of Product Structure Integrates Kansei Design Evaluation Identification on Creation of New Products

Integrates of product structure design and kansei design, this Is the product ”inner beauty and outer beauty, the united design, this way can make a complete quality of new product design, fulfill the psychological needs of consumers, Is also a good way to promote product sales. This paper from new product design analysis of the kansei words, can promote the product structure evaluation of the kansei components, and combine the importance and the order of the kansei words of the product structure. This research picked up the people who had the product design experiences from different enterprises, and the standard of the evaluation is AHP (Analytical Hierarchy Process), the GRA (Grey Structural Modeling) was also used to analyze the importance and order of the perceptual words. Plus, GSM (Grey Structural Modeling) was used to explain the results of the research, and available to the application on references of new product design.

Using fuzzy reasoning techniques and the domain ontology for anti-diabetic drugs recommendation

In this paper, we use fuzzy reasoning techniques and the domain ontology for anti-diabetic drugs selection. We present an anti-diabetic drugs recommendation system based on fuzzy rules and the anti-diabetic drugs ontology to recommend the medicine and the medicine information. The experimental results show that the proposed anti-diabetic drugs recommendation system has a good performance for anti-diabetic drugs selection.

GSM-RGSM Software and Its Application in Educational Measurement

Grey Structural Modeling (GSM) is an effective tool for analyzing complex systems and has been used in many educational researches in recent years. This study developed a tool to determine the relational structure between items and relational structure between students according to the GSM method. GSM-RGSM software, which is the result of this study, was developed by the MATLAB language. The software processes the data which is the result of a text and then provides results in graphs. GSM-RGSM software will help the user to reduce the complex mathematical calculations when using GSM method and provide results accurately and quickly. A graphical user interface is also designed to help users quickly become familiar and easy to use software. This software also provides RGSM graph, which is considered as a special case of the GSM. In this paper, a practical example of using GSM-RGSM software to analyze math test results is used as illustration for the application of the software in educational