The Study Of QoS Multicast Routing Based On Chaotic Neural Networks

Multicast refers to the transmission of information from an information source to a number of destination nodes, and Qo S is called Quality of Sevice, it is a kind of network security mechanism, which is used to solve the problem of network delay and congestion. It is a kind of ability of network to provide higher priority service. With the emergence of a large number of new network services, the multicast technology with Qo S guarantees has become a hot research topic. Qo S multicast routing problem is also called Steiner tree problem, which is used to minimize the cost of multicast tree, which has been proved to be NP complete problem. Select the appropriate Qo S multicast routing algorithm is of great significance for the high quality of multicast communication, the chaotic neural network algorithm is an effective method for solving such problems.Previous chaotic neural network to solve Qo S multicast routing problem focused on improveing neural network structure to enhance the performance of the algorithm, while ignoring the improvement of energy function, cannot constrain the output matrix "row" and "column" strictly. In this paper, two new constraints are added on the basis of traditional energy function, and a new energy function is constructed to ensure the effectiveness of the closed path. The improved energy function and the transient chaos neural network are combined to solve Qo S multicast routing problem. The simulation results show that the improved algorithm can effectively improve the network convergence to the optimal solution of the probability and speed, and at the same time, it can be applied to the different degree of complexity of the multicast network.Noisy chaotic neural network is obtained by adding exponentially decaying noise on the basis of transient chaotic neural network. In this paper, the improved energy function and the noise chaotic neural network are combined to solve the Qo S multicast routing problem. The simulation results show that the noisy chaotic neural network can increase the effective solution rate and the optimal solution rate, but the improvement of the stochastic noise is different for different reasons. At the same time, the initial noise amplitude and the simulated annealing speed must be controlled within the appropriate range, otherwise the optimization effect will be reduced.Hysteretic noisy chaotic neural network can not only show the stochastic chaotic simulated annealing, but also show the hysteresis dynamics, which can help the neural network to jump out of local extremum, and noise-tuning-based hysteretic noisy chaotic neural network obtained on the basis of Noisy chaotic neural network can be implemented to control the level of random noise. In this paper, Hysteretic noisy chaotic neural network, noise-tuning-based hysteretic noisy chaotic neural network and improved energy function will be applied to Qo S multicast routing problem. The simulation results show that under high noise conditions, anticlockwise hysteretic noisy chaotic neural networks optimization results are better than that of noisy chaotic neural networks, and in low noise conditions clockwise hysteretic noisy chaotic neural network should be adopted to improve the optimization results. Noise-tuning-based hysteretic noisy chaotic neural network has stronger hysteretic dynamics, both high and low levels of noise can by controlling the noise tuning factor to obtain better optimization results than hysteretic noisy chaotic neural networks and noisy neural networks...