S. Rout¹, S. Chakraverty^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 947-980, 2019, DOI:10.32604/cmes.2019.08036

Abstract The dynamic analysis of damped structural system by using finite element method leads to nonlinear eigenvalue problem (NEP) (particularly, quadratic eigenvalue problem). In general, the parameters of NEP are considered as exact values. But in actual practice because of different errors and incomplete information, the parameters may have uncertain or vague values and such uncertain values may be considered in terms of fuzzy numbers. This article proposes an efficient fuzzy-affine approach to solve fully fuzzy nonlinear eigenvalue problems (FNEPs) where involved parameters are fuzzy numbers viz. triangular and trapezoidal. Based on the parametric form, fuzzy numbers have been transformed into… More >

Ş. Emrah Amrahov¹, N. A. Gasilov², A. G. Fatullayev²

CMES-Computer Modeling in Engineering & Sciences, Vol.99, No.5, pp. 351-369, 2014, DOI:10.3970/cmes.2014.099.351

Abstract In this paper, we present a new approach to a time-optimal control problem with uncertainties. The dynamics of the controlled object, expressed by a linear system of differential equations, is assumed to be crisp, while the initial and final phase states are fuzzy sets. We interpret the problem as a set of crisp problems. We introduce a new notion of fuzzy optimal time and transform its calculation to two classical time-optimal control problems with initial and final sets. We examine the proposed approach on an example which is a problem of fuzzy control of mathematical pendulum. More >

Nizami Gasilov¹, Sahin Emrah Amrahov² , Afet Golayoglu Fatullayev ˇ ¹, Halil Ibrahim Karaka¸s¹, Ömer Akın³

CMES-Computer Modeling in Engineering & Sciences, Vol.88, No.2, pp. 93-106, 2012, DOI:10.3970/cmes.2012.088.093

Abstract Uncertainties in some parameters of problems of Leontief input-output analysis lead naturally to fuzzy linear systems. In this work, we consider input-output model, where the technology matrix is crisp and the vector of final outputs is fuzzy. The model is expressed by a fuzzy linear system with crisp matrix and with fuzzy right-hand side vector. We apply a geometric method for solving the system. The method finds the solution in the form of a fuzzy set of vectors. The solution set is shown to be a parallelepiped in coordinate space and is expressed by an explicit formula. The features of… More >

N.A. Gasilov¹, I.F. Hashimoglu², S.E. Amrahov³, A.G. Fatullayev¹

CMES-Computer Modeling in Engineering & Sciences, Vol.85, No.4, pp. 367-378, 2012, DOI:10.3970/cmes.2012.085.367

Abstract In this paper, we consider a high-order linear differential equation with fuzzy forcing function and with fuzzy initial values. We assume the forcing function be in a special form, which we call triangular fuzzy function. We present solution as a fuzzy set of real functions such that each real function satisfies the initial value problem by some membership degree. We propose a method to find the fuzzy solution. We present an example to illustrate applicability of the proposed method. More >

M. De Munck¹, D. Moens², W. Desmet³, D.Vandepitte³

CMES-Computer Modeling in Engineering & Sciences, Vol.47, No.2, pp. 119-166, 2009, DOI:10.3970/cmes.2009.047.119

Abstract Deterministic simulation tools enable a very precise simulation of physical phenomena using numerical models. In many real life situations however, a deterministic analysis is not sufficient to assess the quality of a design. In a design stage, some physical properties of the model may not be determined yet. But even in a design ready for production, design tolerances and production inaccuracies introduce variability and uncertainty. In these cases, a non-deterministic analysis procedure is required, either using a probabilistic or a non-probabilistic approach. The authors developed an intelligent Kriging response surface based optimisation procedure that can be used in combination with… More >