基于样本自组织聚类的BP神经网络预测模型

根据实际应用中神经网络训练样本通常具有内在特征和规律性,提出一种基于样本自组织聚类的BP神经网络预测模型。通过自组织竞争网络的聚类特征,改善样本训练对BP网络性能的影响。BP神经网络采用收敛速度较快和误差精度较高的动量—自适应学习速率调整算法。并通过基于这种模型的空气质量预测实验,表明基于样本自组织聚类的BP神经网络预测模型首先会提高收敛速度,其次会减少陷入局部最小的可能,提高预测精度。

BP neural network predicting model based on samples self-organizing clustering

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 ...