The Neural Network Approach To A Parallel Decentralized Network Routing
The Neural Network Approach To A Parallel Decentralized Network Routing
复制标题
并行分散网络路由的神经网络方法
DOI：
10.1016/s0893-6080(97)00121-4
复制
发表时间：
1998
期刊：
影响因子：
--
通讯作者：
S. Mori
中科院分区：
文献类型：
--
作者：
H. Kurokawa;Chun;S. Mori
随着高速光传输和分组交换技术的发展，基于分组的大容量多媒体通信网络将得到迅速发展。在这些网络中的关键问题之一是网络路由，选择路由到目的地的数据包在网络中传输。在大多数以前的工作中，整个网络被映射到一个大尺寸的Hopfield型神经网络。因此，通过该方法的网络路由不会超出集中控制。本文提出了一种并行分散网络路由算法。该模型包括内部连接网络的组的互连，该内部连接网络是完全连接的，并且驻留在通信网络的每个节点处。由于整个系统中每个神经元的动力学都遵循一个唯一的状态方程，我们可以很容易地看到神经元的更新如何映射到真实的世界网络路由问题。最重要的是，由于具有如此高的收敛速度的神经元的动力学，该模型具有在实时应用中实现次优路由解决方案的能力。仿真结果验证了所提方法的有效性。
With the progress of high-speed optical transmission and packet switching, a large capacity packet-based multi-media communication network is expected to spread rapidly. One of the key issues in these networks is the network routing that chooses the route to the destination for packet transmission in the network. In most previous work, the whole network is mapped to a large size-Hopfield-type neural network. Hence, the network routing by this method is not beyond the centralized control. In this paper, a parallel decentralized Network Routing method is presented. The model comprises an interconnection of groups of an intraconnected network, which is fully connected, and resides at each node of the communication network. Since the dynamics of each neuron in the whole system follows a unique state equation, we can see easily how the update of a neuron maps to real world network routing problems. Most important, becauase of the dynamics of the neurons with such a high speed of convergence, the model has the ability to achieve a sub-optimum routing solution in a real-time application. Finally, simulation results validate the proposed method.