Differential Transform Algorithm for Functional Differential Equations with Time-Dependent Delays

Differential transformation method for solving a neutral functional-differential equation with proportional delays

In this article differential transformation method (DTMs) has been used to solve neutral functional-differential equations with proportional delays. The method can simply be applied to many linear and nonlinear problems and is capable of reducing the size of computational work while still providing the series solution with fast convergence rate. Exact solutions can also be obtained from the kno...

Random fractional functional differential equations

In this paper, we prove the existence and uniqueness results to the random fractional functional differential equations under assumptions more general than the Lipschitz type condition. Moreover, the distance between exact solution and appropriate solution, and the existence extremal solution of the problem is also considered.

On the Laplace transform decomposition algorithm for solving nonlinear differential equations

Differentiable Semiflows for Differential Equations with State-dependent Delays

as usual. In more general cases, the right hand side of the differential equation contains more arguments. It also happens that the delay is given only implicitly by an equation which involves the history xt of the state. Differential equations with state-dependent delay share the property that the results on the uniqueness and dependence on initial data from the theory of retarded functional d...

Exact solutions for wave-like equations by differential transform method

Differential transform method has been applied to solve many functional equations so far. In this article, we have used this method to solve wave-like equations. Differential transform method is capable of reducing the size of computational work. Exact solutions can also be achieved by the known forms of the series solutions. Some examples are prepared to show theefficiency and simplicity of th...