关键词: 样本, 自组织竞争网络, BP神经网络, 空气质量

Abstract: Train samples usually have inherent characteristic and regularity according to the neural network in practical application.This paper presents a BP neural network predicting model based on samples self-organizing clustering.The effect of samples training on BP neural network performance with the clustering characteristic of self-organizing competitive network is improved.BP neural network using adaptive learning rate momentum algorithm has fast convergence rate and high error precision.And according to the air quality forecast experiment based on this kind of model，the BP neural network predicting model based on samples self-organizing clustering improves convergence rate at first，secondly reduces the possibility of getting into local minimum，and improves the prediction accuracy.

Key words: samples, self-organizing competitive network, BP neural network, air quality