基于选择性神经网络集成的地基云状识别算法

GROUND NEPHOGRAM RECOGNITION ALGORITHM BASED ON SELECTIVE NEURAL NETWORK ENSEMBLE

针对传统地基云图云状识别模型精度较低的问题,提出一种基于K均值算法的选择性神经网络集成的方法。该方法以BP神经网络集成模型为基础,采用K均值聚类算法选择部分有差异性的个体神经网络进行集成,建立了云状分类模型。通过对云图样本进行仿真实验,结果表明所提出的算法相对于单个BP神经网络及传统的BP_AdaBoost集成算法用于云图的分类,能有效地提高云图识别分类的精度。

In view of the low accuracy of traditional ground nephogram recognition model,the authors put forward a k-means algorithmbased selective neural network ensemble method,which takes BP neural network ensemble model as the basis,uses k-means algorithm to choose the individual neural networks with partial diversities for integration,and builds the cloud form classification model. Through simulation experiments on ground nephogram samples,the results show that the algorithm proposed in the article can effectively improve the classification accuracy of ground nephogram recognition in comparison with applying single BP neural network and traditional BP_Ada Boost ensemble algorithm on classification of ground nephogram.