Syntax-based and semantic image analysis appear as natural and significant fields for applying fuzzy theory, especially in geographical information systems and diagrammatic reasoning. this paper presents fuzzy visual ...
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Syntax-based and semantic image analysis appear as natural and significant fields for applying fuzzy theory, especially in geographical information systems and diagrammatic reasoning. this paper presents fuzzy visual languages as a formal mechanism to analyze images and spatial patterns. Images are described as fuzzy visual sentences using a new class of generative mechanisms, the fuzzy relational adjacency grammars, which provide an algorithmic tool for approximate reasoning. Previous contributions did not link fuzzy logic with fuzzy relations and fuzzy algebraic structures as in the approach presented here. Our approach is also novel in combining formal languages, algebra, and fuzzy theory. Its expressive power comes from considering spatial relations explicitly on a par with graphical elements to describe images beyond and above low-level primitive extraction. Our methods are applicable not only to off-line image processing but also to interactive applications.
this paper deals withthe analysis and design of a class of fuzzy control systems with uncertainty and disturbance. It first analyzes the Mamdani and Takagi-Sugeno type fuzzy models which are widely used in the contro...
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this paper deals withthe analysis and design of a class of fuzzy control systems with uncertainty and disturbance. It first analyzes the Mamdani and Takagi-Sugeno type fuzzy models which are widely used in the control area and argues that both of these fuzzy models cannot represent the uncertainties of a complex system. A new kind of dynamical fuzzy model called uncertain fuzzy model is proposed to represent a complex system which includes both linguistic information and system uncertainties. A new identification approach is then developed for the uncertain fuzzy model. Contrary to the prevailing LS methods, the final identification results are not parameters of a system model, but a feasible set of parameters which is consistent withthe model structure, data and system uncertainties. the identification method is a kind of optimal recursive ellipsoid algorithm which is based on the Khachiyan ellipsoid algorithm in the context of linear programming.
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