摘要
文章针对BP算法收敛速度慢的问题,提出一种基于局部权值阈值调整的BP算法。该算法结合生物神经元学习与记忆形成的特点,针对特定的训练样本,只激发网络中的部分神经元以产生相应的输出,而未被激发的神经元产生的输出则与目标输出相差较大,那么我们就需要对未被激发的神经元权值阈值进行调整。所以该论文提出的算法是对局部神经元权值阈值的调整,而不是传统的BP算法需要对所有神经元权值阈值进行调整,这样有助于加快网络的学习速度。
The paper proposed a BP algorithm based on a partial adjustment of the weight and threshold value. According to the charae-teristics of biological neuron in learning and memory formation, only some neurons were stimulated to produce the output for the specific training samples, while the other part of the neurons weren＇t stimulated. There are large difference between this part of the neuron＇s output and target, and then we need this part neurons weight and threshold value to adjust. Therefore the algorithm proposed in this paper only adjust the weight and the threshold value of the local neurons, and this can accelerate the learning speed of the network.
出处
《计算机与数字工程》
2012年第7期25-27,共3页
Computer & Digital Engineering
关键词
BP神经网络
学习算法
距离
权值阈值调整
13P neural network
learning algorithm
distance
weight and threshold adjustment
