An new back propagation algorithm with chaotic learning rate

BP(Back Propagation) neural network, as a method of data fusion technology, has been used in many common fields widely. While, the main problem of BP algorithm is that the optimal procedure is easily trapped into local minimum value and the speed of convergence is very slow. To avoid this problem, this paper, which, making use of ergodicity property of chaos, starts its improvement from the learning rate. Validity of the proposed method is examined by performing simulations on network traffic prediction, the result shows that the improved algorithm not only is more efficient in internet traffic prediction with higher precision and faster speed of convergence, but also somewhat saves the network from the problem of local minima.