Solving Fractional Order Differential Equations by Using Fractional Radial Basis Function Neural Network

被引:4
|
作者
Javadi, Rana [1 ]
Mesgarani, Hamid [1 ]
Nikan, Omid [2 ]
Avazzadeh, Zakieh [3 ]
机构
[1] Shahid Rajaee Teacher Training Univ, Fac Sci, Tehran 16785163, Iran
[2] Iran Univ Sci & Technol, Sch Math & Comp Sci, Tehran 1684613114, Iran
[3] Univ South Africa, Dept Math Sci, ZA-0003 Florida, South Africa
来源
SYMMETRY-BASEL | 2023年 / 15卷 / 06期
关键词
artificial neural networks; RBF; radial basis function; fractional differential equations; fractional RBF; initial value problems; fractional gradient descent; MODEL; DIFFUSION;
D O I
10.3390/sym15061275
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Fractional differential equations (FDEs) arising in engineering and other sciences describe nature sufficiently in terms of symmetry properties. This paper proposes a numerical technique to approximate ordinary fractional initial value problems by applying fractional radial basis function neural network. The fractional derivative used in the method is considered Riemann-Liouville type. This method is simple to implement and approximates the solution of any arbitrary point inside or outside the domain after training the ANN model. Finally, three examples are presented to show the validity and applicability of the method.
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页数:10
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