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.
为使问题的解能实时、可靠地完成，将一种用于最短路径计算的双层递归神经网络应用于路由选择的流量导数法中。
A structure and training algorithm for quasi-diagonal recurrent neural network (QDRNN) is presented.
提出一种准对角递归神经网络(QDRNN)结构及学习算法。
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.
辨识结果表明，动态递归网络模型优于传统辨识模型，适于非线性、不确定结构的辨识。
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.
辨识结果表明，动态递归网络模型优于传统辨识模型，适于非线性、不确定结构的辨识。
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