In this paper,the homtopy perturbation method (HPM) was applied to obtain the approximate solutions of the fractional order integro-differential equations . The fractional order derivatives and fractional order integral are described in the Caputo and Riemann-Liouville sense respectively. We can easily obtain the solution from convergent the infinite series of HPM . A theorem for convergence and error estimates of the HPM for solving fractional order integro-differential equations was given. Moreover, numerical results show that our theoretical analysis are accurate and the HPM can be considered as a powerful method for solving fractional order integro-diffrential equations.
Y Adnan, H Atiyah, IH Neamah…, International Development Planning Review, 2024
The main purpose of the work is to analyse studies of themagnetohydrodynamic “MHD” flow for a fluid of generalized Burgers’ “GB” within an annular pipe submitted under impulsive pressure “IP” gradient. Closed form expressions for the velocity profile, impulsive pressure gradient have been taken by performing the finite Hankel transform “FHT” and Laplace transform “LT” of the successive fraction derivatives. As a result, many figures are planned to exhibit the effects of (different fractional parameters “DFP”, relaxation and retardation times, material parameter for the Burger’s fluid) on the profile of velocity of flows. Furthermore, these figures are compa
The main purpose of this paper is to define generalized Γ-n-derivation, study and investigate some results of generalized Γ-n-derivation on prime Γ-near-ring G and
This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi