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A least squares support vector fuzzy regression model(LS-SVFR) is proposed to estimate uncertain and imprecise data by applying the fuzzy set principle to weight vectors.This model only requires a set of linear equations to obtain the weight vector and the bias term,which is different from the solution of a complicated quadratic programming problem in existing support vector fuzzy regression models.Besides,the proposed LS-SVFR is a model-free method in which the underlying model function doesn’t need to be predefined.Numerical examples and fault detection application are applied to demonstrate the effectiveness and applicability of the proposed model.
A least squares support vector fuzzy regression model (LS-SVFR) is proposed to estimate uncertain and imprecise data by applying the fuzzy set principle to weight vectors. This model only requires a set of linear equations to obtain the weight vector and the bias term, which is different from the solution of a complicated quadratic programming problem in existing support vector fuzzy regression models .esides, the proposed LS-SVFR is a model-free method in which the underlying model function does not need to be predefined. Numerical examples and fault detection application are applied to demonstrate the effectiveness and applicability of the proposed model.