一种改进的反向传播神经网络算法

An Improved Back-Propagation Neural Network Algorithm

在标准反向传播神经网络算法的基础上,提出了一种改进的反向传播神经网络算法.通过对每个处理单元增加3个参数来增强作用函数,且3个参数与连接权一样,在学习过程中进行实时更新.此算法提高了学习速度,且减少了进入局部最小点的可能性.通过XOR问题的仿真证明了改进算法的有效性.

Based on the idea of standard back-propagation(BP) learning algorithm, an improved BP learning algorithm is presented. Three parameters are incorporated into each processing unit(PU) to enhance the output function. The improved BP learning algorithm is developed for updating the three parameters as well as the connection weights. It not only improves the learning speed, but also reduces the occurrence of local minima. Finally, the algorithm is tested on the XOR problem to verify the validity of the improved BP.