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This paper studies the control problem of continuous interval type 2 fuzzy singular fractional-order systems with order 0 < α < 1. A dynamic output feedback control strategy is proposed to solve it. The advantages of our approach with respect to the existing results are threefold. First, the control design allows mismatched membership functions between the controller and the plant, thus enhancing its universality. Second, some specific assumptions on the system matrix are removed, hence improving the generality of our approach. Third, for ease of calculation of the controller gains, the equivalent transformation method and the linear matrix inequality technique are adopted, instead of iterative algorithms or singular value decomposition technique. The simulation results are given to show the effectiveness and the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

When the observed data are imprecise, the uncertain regression model is more suitable for the linear regression analysis. Least squares estimation can fully consider the given data and minimize the sum of squares of residual error and can effectively solve the linear regression equation of imprecisely observed data. On the basis of uncertainty theory, this paper presents an equation deformation method for solving unknown parameters in uncertain linear regression equations. We first establish the equation deformation method of one-dimensional linear regression model and then extend it to the case of multiple linear regression model. We also combine the equation deformation method with Cramer's rule and matrix and propose the Cramer's rule and matrix elementary transformation method to solve the unknown parameters of the uncertain linear regression equation. Numerical example show that the equation deformation method can effectively solve the unknown parameters of the uncertain linear regression equation. [ABSTRACT FROM AUTHOR]