In this paper, we consider an approximate analytical method of optimal homotopy asymptotic method-least square (OHAM-LS) to obtain a solution of nonlinear fractional-order gradient-based dynamic system (FOGBDS) generated from nonlinear programming (NLP) optimization problems. The problem is formulated in a class of nonlinear fractional differential equations, (FDEs) and the solutions of the equations, modelled with a conformable fractional derivative (CFD) of the steepest descent approach, are considered to find the minimizing point of the problem. The formulation extends the integer solution of optimization problems to an arbitrary-order solution. We exhibit that OHAM-LS enables us to determine the convergence domain of the series solution obtained by initiating convergence-control parameter C-j ' s. Three illustrative examples were included to show the effectiveness and importance of the proposed techniques.

In this paper, an approximate analytical solution of fractional 2D Navier-Stokes equation (NSE) is obtained using homotopy-perturbation method (HPM). The fractional 2D NSE can be represented in the form of fractional Riccati differential equation (FRDE). Then the FRDE is solved by HPM. The HPM solution is almost equal to the exact solution when α=1. A numerical example is given to illustrate the proposed method.

A numerical solution of fuzzy quadratic Riccati differential equation is estimated using a proposed new approach for neural networks (NN). This proposed new approach provides different degrees of polynomial subspaces for each of the transfer function. This multitude of transfer functions creates unique agents in the structure of the NN. Hence it is named as multiagent neuroapproach (multiagent NN). Previous works have shown that results using Runge-Kutta 4th order (RK4) are reliable. The results can be achieved by solving the 1st order nonlinear differential equation (ODE) that is found commonly in Riccati differential equation. Multiagent NN shows promising results with the advantage of continuous estimation and improved accuracy that can be produced over Mabood et al. (2013), RK-4, and the existing neuromethod (NM). Numerical examples are discussed to illustrate the proposed method.