This paper derives an estimated function made by simple Neural Network to find initial state of optimization parameters. It changes a system of differential equations with boundary values to a system of equations with initial values. So a lot of time would be saved to solve it. As a result, the system with differential equations will reach the desired final state.

1.
Lagaris
I. E.
,
Likas
A. C.
,
Papageorgeou
D. G.
, September
2000
, “
Neural Network Methods for Boundary Value problems with Irregular Boun-daries
”,
IEEE Transactions on Neural Networks
, Vol.
11
, No.
5
, pp
1041
1049
.
2.
Chong
E. K. P.
,
Hui
S.
,
Zak
S. H.
, November
1999
, “
An Analysis of a Class of Neural Networks for Solving Linear Programming Problems
”,
IEEE Transactions on Automatic Control
, Vol.
44
, No.
11
, pp
1995
2006
.
3.
Ferrari
S.
,
Stengel
R. F.
, Janurary
2005
, “
Smooth Function Approximation using Neural Networks
”,
IEEE Transactions on Neural Networks
, Vol.
16
, No.
1
, pp
24
38
.
4.
A. R. Babakhani, January 2006, “Guidance and Control of AUVs while Moving Obstacles”, MSc thesis, Sharif University of Technology, Tehran, Iran.
5.
Byron S.Gottfried, Joel Weisman, 1973, “Introduction to Optimization Theory”, Department of Industrial Engineering, University of Pittsburg,.
6.
Unknown Author, 2003, “Formula Sheet for Optimal Control”, Devision of Optimization and Systems Theory, Royal Institute of Technology, 10044 Stockholm, Sweden.
7.
Morton.M.Denn, 1969, “Optimization by Variational Methods”, Department of Chemical Engineering, University of Delaware.
8.
Laurene Fausett, 1994, “Fundamentals of Neural Networks-”, Florida Institute of Technology.
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