关键词: BP神经网络, 花授粉算法, 转换概率, 变异因子

Abstract: The standard BP neural networks adjusts the weights and threshold value by the gradient descent method, which is easy to fall into the local optimum and slow convergence rate. Thus, the application of BP neural network is limited. In this paper, a Self-adaptive Flower Pollination Algorithm（SFPA） is proposed and used to optimize the weights and threshold value of BP neural networks. Firstly, the self-adaptive SFPA is put forward by adjusting the switch probability of FPA during iterations and as in the mutation operator. Then, the SFPA is integrated with the BP in two different forms：SFPA1-BP and SFPA2-BP. Finally, the performance of the new BP network is tested by using function approximation experiments and Iris classification data set. The?study shows that SFPA1-BP and SFPA2-BP are superior to other networks, especially in the function approximations and classification.

Key words: Back-Propagation neural network, Flower Pollination Algorithm（FPA）, switch probability, mutation operator