Stochastic numerical treatment for solving Falkner-Skan equations using feedforward neural networks.

In this article, the artificial intelligence techniques have been used for the solution of Falkner-Skan (FS) equations based on neural networks optimized with three methods including active set technique, sequential quadratic programming and genetic algorithms (GA) hybridization. Log-sigmoid activation function is used in artificial neural network architecture. The proposed techniques are applied to a number of cases for Falkner-Skan problems, and results were compared with GA hybrid results in all cases and were found accurate. The level of accuracy is examined through statistical analyses based on a sufficiently large number of independent runs. [ABSTRACT FROM AUTHOR]