(untitled) (JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences, 2018)
In this paper we presented a new way based on neural network has been developed for solutione of two dimension partial differential equations . A modified neural network use to over passing the Disadvantages of LM algorithm, in the beginning we suggest signaler value decompositions of Jacobin matrix (J) and inverse of Jacobin matrix( J-1), if a matrix rectangular or singular Secondly, we suggest new calculation of μk , that ismk=|| E (w)||2 look the nonlinear execution equations E(w) = 0 has not empty solution W* and we refer to the second norm in all cases ,whereE(w): is continuously differentiable and E(x) is Lipeschitz continuous, that is=|| E(w 2)- E(w 1)||£ L|| w 2- w 1|| ,where L is Lipeschitz constant.