I'm taking a course in adaptive neural networks and became interested in trying to see if I can use neural networks to find the optimum values for the control parameters in atmospheric re-entry in order to reach a specified target on Earth. I've come across a paper that discusses this and goes into some detail. First and foremost, I need to determine the bounds on the re-entry flight, i.e max and min entry angle to avoid skip-out and excessive g-forces. I have the equations of motion setup as well as penalty functions for altitude and for pilot g-force loading. The steepness of the entry angle is determined by increasing the entry angle from 90 to 180 in small steps and solving the pilot penalty minimization problem. The upper bound is the largest entry angle that yields a pilot penalty of no more than 1. I am getting different results from what is shown in the paper. Procedure: The program takes initial values for the state variables and uses them in the numerical integration. Numerical integration is stopped when the difference (1 - pilot penalty) <= 0.01 and when the velocity <= Mach 2. This is repeated for the next theta value. I am increasing theta by 1 degree and the time steps by 0.01. Once I have all the entry angles for the pilot penalty minimization, I then search for the largest of these. This will then be the upper bound. The files for this project as well as the paper if anyone is interested are here http://www39.zippyshare.com/v/22155505/file.html. The section in the paper that I am working on right now are pages 15-29. If someone could take a look at my program and see if I messed up anywhere that would be awesome.