状变动态选路的神经网络算法

State Dependent Routing with a Neural Networks Algorithm

吸收一些电路交换网动态选路算法的优点，提出了基于神经网络Ｈｏｐｆｉｅｌｄ模型的状变实时选路算法．该模型全面集成了网管中心监控的话务数据、网路状态和电路保留策略，在对中继线群阻塞概率短期预测的基础上优化全网动态路由．模型的设计尽量接近实际，又充分考虑硬件的可实现性．计算机模拟结果表明，该算法明显优于传统的固定路由选路法，为神经网络在动态选路中应用提出了一种解决方案．

A state dependent routing algorithm based on Hopfield's neural networks model is given, which takes advantage of other dynamic routing methods for the circuit switched network. The model fully integrates the traffic data, network states and circuits reservation policy monitored by the network management center, and optimizes dynamic routes for the whole network on the bases of short period forecasting of trunk congestion probabilities. The design of the model is as closer to the real world as possible, and also takes the hardware realization into consideration. Computer simulations show that the algorithm is much better than the classical fixed routing methods, therefore the model could be a solution for the applications of neural networks to the dynamic routing.