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Certain Algorithms for Modeling Uncertain Data Using Fuzzy Tensor Product B é zier Surfaces

机译:使用模糊张量积Bézier曲面对不确定数据建模的某些算法

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Real data and measures are usually uncertain and cannot be satisfactorily described by accurate real numbers. The imprecision and vagueness should be modeled and represented in data using the concept of fuzzy numbers. Fuzzy splines are proposed as an integrated approach to uncertainty in mathematical interpolation models. In the context of surface modeling, fuzzy tensor product B é zier surfaces are suitable for representing and simplifying both crisp and imprecise surface data with fuzzy numbers. The framework of this research paper is concerned with various properties of fuzzy tensor product surface patches by means of fuzzy numbers including fuzzy parametric curves, affine invariance, fuzzy tangents, convex hull and fuzzy iso-parametric curves. The fuzzification and defuzzification processes are applied to obtain the crisp Beziér curves and surfaces from fuzzy data points. The degree elevation and de Casteljau’s algorithms for fuzzy B é zier curves and fuzzy tensor product B é zier surfaces are studied in detail with numerical examples.
机译:实际数据和度量通常不确定,无法用准确的实数令人满意地描述。不精确性和模糊性应使用模糊数的概念进行建模并在数据中表示。提出了模糊样条作为数学插值模型中不确定性的综合方法。在曲面建模的上下文中,模糊张量积Bézier曲面适用于表示和简化带有模糊数的清晰和不精确曲面数据。本文的框架通过模糊数涉及模糊张量积曲面补丁的各种性质,包括模糊参数曲线,仿射不变性,模糊切线,凸包和模糊等参曲线。应用模糊和反模糊处理从模糊数据点获得清晰的贝塞尔曲线和曲面。通过数值示例详细研究了模糊Bézier曲线和模糊张量积Bézier曲面的度高和de Casteljau算法。

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