This study presents a new approach based on Tanaka's fuzzy linear regression (FLP) algorithm to solve well-known power system economic load dispatch problem (ELD). Tanaka's fuzzy linear regression (FLP) formulation will be employed to compute the optimal solution of optimization problem after linearization. The unknowns are expressed as fuzzy numbers with a triangular membership function that h...

We propose a new approach to functional regression based on fuzzy evidence theory. This method uses a training set for computing a fuzzy belief structure which quantiies diierent types of uncertainties, such as nonspeciicity, connict, or low density of input data. The method can cope with a very large class of training data, such as numbers, intervals , fuzzy numbers, and, more generally, fuzzy...

In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the usin...

ÐA fuzzy decision tree is constructed by allowing the possibility of partial membership of a point in the nodes that make up the tree structure. This extension of its expressive capabilities transforms the decision tree into a powerful functional approximant that incorporates features of connectionist methods, while remaining easily interpretable. Fuzzification is achieved by superimposing a fu...

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the Lagrangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the crossvalidation procedure for selecting the o...

When there are interactions among the independent variables, the performances of the most often used multiple regression models and ridge regression model are not well enough. In contrast, the Choquet integral takes into account the interactions among independent variables, the discrete Choquet integral regression models based on some non-negative valued fuzzy measures or monotonic measure can ...

This study provides a principal component analysis-fuzzy-support vector regression model for stock price prediction. Stocks with similar historical trends are selected using principal component analysis. Fuzzy information granulation is performed to construct a probability density for stock prices. Support vector regression is implemented to generate a regression function for future price predi...