基于智能复合结构的可靠性分布模式自动识别

 采用Vor onoi 向量对SOM 网络算法进行了改进, 提高了学习收敛速度。通过提取数据的统计特征,建立了可靠性分布模式自动识别样本。提出的智能自动识别模型分两层, 在SOM 网络层对概率分布模式进行自动聚类, 在支持向量机层对各聚类组进行分类学习和识别, 获得识别模型的双层记忆权值。最后采用模型对常用可靠性分布模式进行了自动识别研究。测试结果表明, 建立的可靠性分布模式自动识别模型是可行、有效的。

Reliability Distributions Automatic Identification Based on Intelligent Combined Structure Model

An intelligent identification combined structure model is proposed using self-organizing map (SOM) and support vector machines (SVM). This model can improve the self-organizing map algorithm using Voronoi vector to reduce space occupation and improve convergence, and develop probability intelligent identification training samples set. Due to the complexity of the summary statistics, the authors select kurtosis, skewness, quantile and cumulative probability as parameters for data distributions identification training sets in experience. The combined structure model is divided into two layers. In the first SOM layer, different reliability distributions training sets are clustered into groups using SOM. In the second SVM layer, the clusters are learned and classified respectively in each group using novel multi-class support vector machines. Random data time series of 23 types of probability distributions are testing identified in the trained model. The results indicate that the identification rates are higher by the intelligent model compared to BP neural networks and probability networks models.