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Wei Xiao - One of the best experts on this subject based on the ideXlab platform.
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fractal dimension of riemann liouville fractional integral of certain unbounded variational Continuous Function
Fractals, 2017Co-Authors: Yang Li, Wei XiaoAbstract:In the present paper, a one-dimensional Continuous Function of unbounded variation on the interval [0, 1] has been constructed. Box dimension of this Function has been proved to be 1. Furthermore, Box dimension of its Riemann–Liouville fractional integral of any order has also been proved to be 1.
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FRACTAL DIMENSION OF RIEMANN–LIOUVILLE FRACTIONAL INTEGRAL OF CERTAIN UNBOUNDED VARIATIONAL Continuous Function
Fractals, 2017Co-Authors: Yang Li, Wei XiaoAbstract:In the present paper, a one-dimensional Continuous Function of unbounded variation on the interval [0, 1] has been constructed. Box dimension of this Function has been proved to be 1. Furthermore, Box dimension of its Riemann–Liouville fractional integral of any order has also been proved to be 1.
Yang Li - One of the best experts on this subject based on the ideXlab platform.
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fractal dimension of riemann liouville fractional integral of certain unbounded variational Continuous Function
Fractals, 2017Co-Authors: Yang Li, Wei XiaoAbstract:In the present paper, a one-dimensional Continuous Function of unbounded variation on the interval [0, 1] has been constructed. Box dimension of this Function has been proved to be 1. Furthermore, Box dimension of its Riemann–Liouville fractional integral of any order has also been proved to be 1.
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FRACTAL DIMENSION OF RIEMANN–LIOUVILLE FRACTIONAL INTEGRAL OF CERTAIN UNBOUNDED VARIATIONAL Continuous Function
Fractals, 2017Co-Authors: Yang Li, Wei XiaoAbstract:In the present paper, a one-dimensional Continuous Function of unbounded variation on the interval [0, 1] has been constructed. Box dimension of this Function has been proved to be 1. Furthermore, Box dimension of its Riemann–Liouville fractional integral of any order has also been proved to be 1.
Yong Shun Liang - One of the best experts on this subject based on the ideXlab platform.
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box dimensions of riemann liouville fractional integrals of Continuous Functions of bounded variation
Nonlinear Analysis-theory Methods & Applications, 2010Co-Authors: Yong Shun LiangAbstract:Abstract If a Continuous Function f ( x ) has bounded variation on the unit interval [ 0 , 1 ] , the box dimension of f ( x ) is 1. Furthermore, the box dimension of a Riemann–Liouville fractional integral of f ( x ) is still 1.
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Box dimensions of Riemann–Liouville fractional integrals of Continuous Functions of bounded variation
Nonlinear Analysis-theory Methods & Applications, 2010Co-Authors: Yong Shun LiangAbstract:Abstract If a Continuous Function f ( x ) has bounded variation on the unit interval [ 0 , 1 ] , the box dimension of f ( x ) is 1. Furthermore, the box dimension of a Riemann–Liouville fractional integral of f ( x ) is still 1.
Valentin A. Skvortsov - One of the best experts on this subject based on the ideXlab platform.
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Divergent Walsh-Fourier Series. Almost Everywhere Convergence of Walsh-Fourier Series of L2 Functions
Mathematics and Its Applications, 1991Co-Authors: B. Golubov, A. Efimov, Valentin A. SkvortsovAbstract:In Chapter 2 we saw that even for a Continuous Function it is necessary to impose additional conditions to insure that its Walsh- Fourier series converges at every point. Without such conditions, as we remarked in §2.3, the Fourier series of a Continuous Function may diverge at some points.
Quanke Pan - One of the best experts on this subject based on the ideXlab platform.
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an improved fruit fly optimization algorithm for Continuous Function optimization problems
Knowledge Based Systems, 2014Co-Authors: Quanke Pan, Hongyan Sang, Junhua Duan, Liang GaoAbstract:Abstract This paper presents an improved fruit fly optimization (IFFO) algorithm for solving Continuous Function optimization problems. In the proposed IFFO, a new control parameter is introduced to tune the search scope around its swarm location adaptively. A new solution generating method is developed to enhance accuracy and convergence rate of the algorithm. Extensive computational experiments and comparisons are carried out based on a set of 29 benchmark Functions from the literature. The computational results show that the proposed IFFO not only significantly improves the basic fruit fly optimization algorithm but also performs much better than five state-of-the-art harmony search algorithms.
