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Finite Volume Element Method for Fractional Order Neutral Time-Delay Differential Equations
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作者 Zicheng Wei Qing Yang 《Engineering(科研)》 2025年第1期30-52,共23页
Fractional-order time-delay differential equations can describe many complex physical phenomena with memory or delay effects, which are widely used in the fields of cell biology, control systems, signal processing, et... Fractional-order time-delay differential equations can describe many complex physical phenomena with memory or delay effects, which are widely used in the fields of cell biology, control systems, signal processing, etc. Therefore, it is of great significance to study fractional-order time-delay differential equations. In this paper, we discuss a finite volume element method for a class of fractional-order neutral time-delay differential equations. By introducing an intermediate variable, the fourth-order problem is transformed into a system of equations consisting of two second-order partial differential equations. The L1 formula is used to approximate the time fractional order derivative terms, and the finite volume element method is used in space. A fully discrete format of the equations is established, and we prove the existence, uniqueness, convergence and stability of the solution. Finally, the validity of the format is verified by numerical examples. 展开更多
关键词 Fractional Order Time-Delay Differential Equation Finite Volume Element Method l1 approximation error Estimation Numerical Simulation
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Statistical Classification Using the Maximum Function 被引量:1
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作者 T. Pham-Gia Nguyen D. Nhat Nguyen V. Phong 《Open Journal of Statistics》 2015年第7期665-679,共15页
The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theore... The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theoretical results on this function, and then its application in classification using a computer program we have developed. This approach leads to clear decisions, even in cases where the extension to several classes of Fisher’s linear discriminant function fails to be effective. 展开更多
关键词 MAXIMUM DISCRIMINANT Function Pattern Classification NORMAl Distribution BAYES error l1-norm linear QUADRATIC Space CURVES
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