A Takagi–Sugeno model with fuzzy inputs viewed from multidimensional interval analysis.pdfVIP
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A Takagi–Sugeno model with fuzzy inputs viewed from multidimensional interval analysis.pdf
Fuzzy Sets and Systems 135 (2003) 39 – 61
/locate/fss
A Takagi–Sugeno model with fuzzy inputs viewed from multidimensional interval analysis
Felipe Ferna?ndez ?, Julio Gutie?rrez
Dep. Tecnolog?a Foto?nica, Facultad de Informa?tica, Universidad Polite?cnica de Madrid, 28660 Madrid, Spain
Abstract
Takagi–Sugeno (T–S) fuzzy systems have been successfully applied to a wide range of problems and have demonstrated signi?cant advantages in nonlinear control. This paper presents a fuzzi?ed T–S interpolation– approximation system mainly based on the speci?cation of a multidimensional crisp partition that de?nes the corresponding local regions (multidimensional intervals) where the corresponding rules apply, the related output functions and a reduced set of global fuzzy parameters. These fuzzy parameters capture the di erent uncertainties of a fuzzy system: imprecision of inputs, vagueness of antecedent linguistic labels and smoothness requirements of outputs. This approach makes easier the design of a zero-order product–sum T–S system with fuzzi?ed inputs, fuzzi?ed antecedent crisp partition, and outputs with an additional spatial output ?lter. Convolution operations applied on an equalized and normalized input domain are considered to specify the corresponding fuzzi?cation of a crisp partition. The kernels of these convolutions are even B-spline functions of order n, constructed from a n-fold convolution of an even interval characteristic function. We use a correctness-preserving transformation to simplify the output computation: a global transformation of imprecision of inputs, vagueness of antecedent terms and smoothness requirements of outputs into a set of o -line convolution operations applied to the corresponding antecedent crisp partition. By this method a fuzzi?ed zero-order T–S system de?ned on a multidimensional crisp partition is directly transformed into a multidimensional spline interpolator–approximator by means of fuzzy operations and an equalization–normaliz
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