Abstract
Multimedia communications have become popular in many network services, such as video conferencing, video on demand, and so on. Most multimedia applications require that the attached hosts/routers transmit data through multicasting. In order to provide efficient data routing, routers must provide the multicast capability. In this paper, a self-feedback mechanism controlled by an annealing strategy and embedded into the Hopfield neural network is proposed to calculate the shortest-path tree for the Multicast Open-Shortest Path First (MOSPF) Protocol. A multicast shortest path tree is built on demand and is rooted in the source node. To facilitate hardware implementation, the annealed chaotic neural network can be employed to deal with shortest-path (SP) problems in packet switching computer networks. In addition, the annealed chaotic neural network can avoid the local-minima solution so as to obtain near-global minima or global-minimum solutions.Multimedia communications have become popular in many network services, such as video conferencing, video on demand, and so on. Most multimedia applications require that the attached hosts/routers transmit data through multicasting. In order to provide efficient data routing, routers must provide the multicast capability. In this paper, a self-feedback mechanism controlled by an annealing strategy and embedded into the Hopfield neural network is proposed to calculate the shortest-path tree for the Multicast Open-Shortest Path First (MOSPF) Protocol. A multicast shortest path tree is built on demand and is rooted in the source node. To facilitate hardware implementation, the annealed chaotic neural network can be employed to deal with shortest-path (SP) problems in packet switching computer networks. In addition, the annealed chaotic neural network can avoid the local-minima solution so as to obtain near-global minima or global-minimum solutions.