关键词: BP算法, 学习率, 误差函数, 激励函数

Abstract: In view of the BP neural network existence convergence rate slow and easy to fall into local minimum，this paper presents an adaptive learning rate adjustment and dynamic adjustment S-type activation function combination of improved BP algorithm.The proposed algorithm connects the learning rate with the error function and the slope of the activation function of each hidden and output unit is automatically adjusted.Finally through the example simulation，the validity of the proposed method is verified compared to the standard BP algorithm，the momentum method and adaptive learning rate method.The experimental results show that the joint optimization of BP algorithm can effectively speed up the network convergence process and has strong generalization ability.

Key words: back-propagation algorithm, learning rate, error function, activation function