【摘要】 探讨了在高速包交换计算机网络中 ,具有端到端时延及时延抖动限制的组播路由问题 ,提出了基于混沌神经网络的组播路由优化算法 .所提出的方法具有许多优良特性 ,即暂态混沌特性和平稳收敛特性 ,能有效地避免传统 Hopfield神经网络极易陷入局部极值的缺陷 .它通过短暂的倒分叉过程 ,能很快进入稳定收敛状态 .通过计算机仿真 ,和其它的一些方法进行了对比 ,结果表明 :该算法能根据组播应用对时延和时延抖动的要求 ,快速、有效地构造最优组播树 ,具有较强的实时性 .更多还原

【Abstract】 Recently, QoS(Quality of Service) multicast routing is a "hot-spot" in computer and communication networks. Many researches are done in this topic. Intelligent techniques, such as neural networks, is a good method for the combination optimization problems. And its applications in the routing problem are more and more popular. In this paper, we study the end-to-end delay- and delay variation-constrained least-cost multicast routing problem in high speed packet-switched networks, which is known to be NP-complete. And a new algorithm based on chaotic neural networks is presented to optimize the multicast tree with delay- and delay variation-constraint. The proposed algorithm includes two steps: (1) Using the Dijkstra algorithm to compute the candidate routes; (2) Constructing the chaotic neural network to optimize the candidate routes and get the optimal multicast tree. The used chaotic neural networks has many merits, such as transient chaos and stable convergence etc., and it can effectively overcome the drawbacks of easily getting stuck in local minim in conventional Hopfield neural networks. It can reach a stable convergent state after shortly reversed bifurcation. Through the computer simulation, this algorithm is compared against other algorithms. The results show that the proposed algorithm is both efficient and effective in constructing the optimal delay- and delay variation-constrained multicast tree in high-speed networks. And it has better real-time property.更多还原