This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression an...

Since Tanaka et al. in 1982 proposed a study in linear regression with a fuzzy model, fuzzy regression analysis has been widely studied and applied in various areas. However, Tanaka’s approach may give an incorrect interpretation of the fuzzy linear regression results when outliers are present in the data set. To handle the outlier problem, we propose an omission approach for Tanaka’s linear pr...

Coronary heart disease is a major cause of morbidity and mortality in the modern society. Many risk factors for coronary heart disease have been discussed and identified by medical fraternity. However, the magnitudes of risk factors, particularly in predicting the disease are remained unknown and inconclusive. The purpose of this study was to develop fuzzy regression prediction model and to inv...

The present paper discusses on development of fuzzy rule based models (FRBMs) for predicting cutting force and surface roughness in milling operation. The models use TakagiSugeno-Kang-type (TSK-type) fuzzy rule to study the effect of four (input) cutting parameters (cutting speed, feed rate, radial depth of cut and axial depth of cut) on outputs (cutting force and surface roughness). The approp...