Application And Research Of Chaotic Neural Networks Algorithm In Combinatorial Optimization Problem

Artificial neural network is to be developed for the study intelligence process of human, focus on the problems of learning method in object oriented machine and constructing learning machine. Chaos is an inside representation of stochastic process in nonlinear determinate system, so recently chaotic neural network has attracted much more attention of researcher, and made much more important progress in the field.Chaos is a nonlinear dynamics system, and the Hopfield framework can be used to make the good combination of neural network and nonlinear dynamical behavior. So it can be used as basic network framework of chaotic neural networks.Because an artificial neural network with chaotic character has more complicated dynamics property, which is different from the traditional neural network. The chaotic neural network has omnidirectional behavior and more complex dynamics property, and has diversified attractors. It is just the dynamics that make it possible for the network to be a technology with popularized application foreground for information processing and optimization computation.The paper study the output function of chaotic neural network more carefully, introduced basic character of chaos and neural networks, research method of constructing chaotic neural networks and discussed its application in combinatorial optimization field.At the same time, the paper study neural network with transient chaos, a kind of chaotic neural network pattern, proposed an adapted discrete and continuous-discrete output functions, we analyzed the feasibility of the new algorithm in principle, compared it with former neural network and discussed the relationship of optimization rate and the time cost of them.At last, the paper applied former chaotic neural network with discrete output function to solve Traveling salesman problem which is the representative question of the combinatorial optimization field. First, we introduced the workload of traditional method. Second, proposed chaotic neural network model which is adapt to TSP. At last, solved TSP with chaotic neural work, and the result illustrated that discrete output function can find global minimum within less time cost and is conformable to be applied in middle-scale TSP problem with much more cities.In the multimedia communications and so on high speed packet-switched networks, the end-to-end delay and delay variation constrained least cost multicast routing problem is a combinatorial optimization problem. The direction of multimedia communications development is how to ensure the (QoS) quality of service of multimedia data and realize its multicast communication. So we study how to apply chaotic neural networks to QoS multicast routing question. We proposed a new delay and delay variation constrained energy function, and adopted it to chaotic neural networks to solve QoS multicast routing problem. The emulation verified that the energy function has an excellent optimization effect, can efficiently draw neuralnetwork to an energy minimum which is corresponding to the optimal result of QoS multicast routing problem.