基于云安全模型的层次泛函网络整体学习算法

Holistic Learning Algorithm for Hierarchical Generalized Networks Based on Cloud Security Model

层次结构的泛函网络结构存在较多的隐变量,导致相同时间周期内的学习预测次数较少,为此设计基于云安全模型的层次泛函网络整体学习算法。采用云服务器获取安全模型运行时的各类信息数据,定义云安全模型内指标的姿态值,并构建姿态值三元约束数值关系,控制网络结构内隐变量数量,构建层次泛函网络逼近数值模型,将同等属性数据集的泛函数值处理为输入单元,输出整体学习算法形成的邻近域。结果表明:所设计的整体学习算法产生的学习预测次数最多,算法的学习预测能力最强。

There are many hidden variables in the hierarchical functional network structure,resulting in less learning predic⁃tion times in the same time period.Therefore,an overall learning algorithm of hierarchical functional network based on cloud securi⁃ty model is designed.The cloud server is used to obtain various information data during the operation of the security model,define the attitude value of the index in the cloud security model,construct the ternary constraint numerical relationship of the attitude val⁃ue,control the number of implicit variables in the network structure,construct the hierarchical functional network approximation numerical model,and process the functional values of the same attribute data set as the input unit,output the adjacent domain formed by the overall learning algorithm.The results show that the designed overall learning algorithm produces the most learning prediction times,and the learning prediction ability of the algorithm is the strongest.