A Multiagent Transfer Function Neuroapproach to Solve Fuzzy Riccati Differential Equations

Abstract

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.