To make the solution be implemented reliably in real time, a neural network for shortest path computation that is a two-layer recurrent structure is applied to flow deviation method.

A structure and training algorithm for quasi-diagonal recurrent neural network (QDRNN) is presented.

Results of identification show that the Elman's recurrent model is superior to the traditional model. It is adaptive to the identification of the non linear and uncertain structure.