Modification of Levenberg-Marquardt Algorithm for Solve Two Dimension Partial Differential Equation

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Khalid Mindeel M. Al-Abrahemee Rana T. Shwayaa

Abstract

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.

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How to Cite
[1]
K. Al-Abrahemee and R. Shwayaa, “Modification of Levenberg-Marquardt Algorithm for Solve Two Dimension Partial Differential Equation”, JUBPAS, vol. 26, no. 7, pp. 107- 117, May 2018.
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