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

Fractional differential equations (FDEs) arising in engineering and other sciences describe nature sufficiently terms of symmetry properties. This paper proposes a numerical technique to approximate ordinary fractional initial value problems by applying radial basis function neural network. The derivative used the method is considered Riemann-Liouville type. simple implement approximates solution any arbitrary point inside or outside domain after training ANN model. Finally, three examples are presented show validity applicability method.