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Stability analysis of fuzzy multivariablesystems: Vector Lyapunov function approach
IEE Proceedings - Control Theory and Applications, 1997The paper describes a design methodology to analyze the stability of a class of nonlinear systems which can be described through a fuzzy dynamic model. The considered model is based on the decomposition of the state space region of the closed loop fuzzy system in \(N\) subregions with \(N\) interacting closed loop subystems. The stability conditions of
Cheng, C. M., Rees, N. W.
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Analysis and design of fuzzy reduced-dimensional observer and fuzzy functional observer
Fuzzy Sets and Systems, 2001The study concerns control problems with observers and a separation principle in Takagi-Sugeno (T-S) fuzzy models. The class of T-S models under consideration assumes the following form (both continuous and discrete-time versions of the model are considered) \[ \text{-if }s_1\text{ is } M_{1i}\text{ and\dots and }s_g\text{ is }M_{pi}\text{ then }dx/dt=
Ma, Xiao-Jun, Sun, Zeng-Qi
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Sensitivity analysis for image represented by fuzzy function
Soft Computing, 2018The standard image representation can be considered as obsolete in image processing area since it was designed mainly to visualize images and not to support image processing algorithms. For that reason, seeking alternative image representations becomes an important issue.
Petr Hurtik +2 more
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International Journal of Computational Methods, 2022
Uncertain structures may exhibit fuzzy uncertainty involving imprecise membership function (FuIMF). In this study, the uncertain parameters in FuIMF case are characterized as fuzzy variables, whereas the key parameters of their membership functions are treated as interval variables rather than exact values.
Lü, Hui +4 more
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Uncertain structures may exhibit fuzzy uncertainty involving imprecise membership function (FuIMF). In this study, the uncertain parameters in FuIMF case are characterized as fuzzy variables, whereas the key parameters of their membership functions are treated as interval variables rather than exact values.
Lü, Hui +4 more
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FUNCTIONAL ANALYSIS AND T-S FUZZY SYSTEM DESIGN
IFAC Proceedings Volumes, 2005This paper introduces the idea of viewing fuzzy modelling and fuzzy system design from a functional analysis perspective. Fuzzy transforms, which are based on the generalised Fourier transform in functional analysis, are proposed. It is demonstrated that, mathematically, a T-S fuzzy model is equivalent to a fuzzy transform.
D.T. Pham, R.X. Qiu
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Model-free functional MRI analysis using improved fuzzy cluster analysis techniques
SPIE Proceedings, 2004Conventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown.
Oliver Lange +5 more
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Information Sciences, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Dong Hwan +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Dong Hwan +2 more
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Analysis of approximation of continuous fuzzy functions by multivariate fuzzy polynomials
Fuzzy Sets and Systems, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Structural analysis of fuzzy controller with gaussian membership function
IFAC Proceedings Volumes, 1999Abstract Based on Mamdani's Max-Min inference operator, this paper presents an approach to the structure analysis of fuzzy controller with normal-distribution-shaped membership function. The authors deduce the output formulas of a typical fuzzy controller with linear control rales.
Xiang-Jie Liu, Xiao-Xin Zhou
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Approximation accuracy analysis of fuzzy systems as function approximators
IEEE Transactions on Fuzzy Systems, 1996This paper establishes the approximation error bounds for various classes of fuzzy systems (i.e., fuzzy systems generated by different inferential and defuzzification methods). Based on these bounds, the approximation accuracy of various classes of fuzzy systems is analyzed and compared.
null Xiao-Jun Zeng, M.G. Singh
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