In order to efficiently improve the prediction accuracy, two load forecasting model based on fuzzy theory are presented, which are fuzzy clustering model and improved fuzzy regression analysis model .The method of fuzzy clustering is used to divide the area by the similar feature of load increasing. The new division is promising to improve the result of evident degree of clustering index to pow...

Logistic regression analysis is used to model categorical dependent variable. It is usually used in social sciences and clinical research. Human thoughts and disease diagnosis in clinical research contain vagueness. This situation leads researchers to combine fuzzy set and statistical theories. Fuzzy logistic regression analysis is one of the outcomes of this combination and it is used in situa...

Fuzzy sets theory has proven over the years to be a valuable tool for modeling uncertainty in engineering. It is used extensively in control, in expert systems and in rule-based models. However, applications to sensitivity analysis and regression are still few, mainly because there is no appropriate software available. A C++ library of objects has been developed to easily and efficiently introd...

In this study, we propose a novel mobile tracking scheme which utilizes the fuzzy-based decision making with the consideration of the information such as previous location, moving direction and distance to the base station as well as received signal strength, thereby resulting to the estimation performance even much better than the previous schemes. Our scheme divides a cell into many blocks ba...

In reliability, quality control and risk analysis, fuzzy methodologies are more and more involved and inevitably introduced difficulties in seeking fuzzy functional relationship between factors. In this paper, we propose a scalar variable formation of fuzzy regression model based on the credibility measure theoretical foundation. It is expecting our scalar variable treatments on fuzzy regressio...

Recent articles, such as McCauley-Bell et al. (1999) and Sánchez and Gómez (2003a, 2003b, 2004), used fuzzy regression (FR) in their analysis. Following Tanaka et. al. (1982), their regression models included a fuzzy output, fuzzy coefficients and an nonfuzzy input vector. The fuzzy components were assumed to be triangular fuzzy numbers (TFNs). The basic idea was to minimize the fuzziness of th...