基于径向基统计网络的数据流分析模型  

Analysis model over data stream based on radial basis statistical network

作　　者：程玉虎[1] 刘博[1] 王雪松[1] 

机构地区：[1]中国矿业大学信息与电气工程学院,江苏徐州221116

出　　处：《控制与决策》2010年第6期879-883,888,共6页Control and Decision

基　　金：国家自然科学基金项目(60804022;60974050);教育部新世纪优秀人才支持计划项目(NCET-08-0836);教育部博士点基金项目(20070290537;200802901506);中国博士后科学基金特别项目(200902533);江苏省自然科学基金项目(BK2008126)

摘　　要：在传统径向基函数网络的基础上,提出一种用于数据流分析的径向基统计网络,采用复合多维高斯函数来拟合输入空间中不同区域的概率密度.基于数理统计方法的径向基统计网络学习算法的时间和空间复杂度,均与输入变量维数和隐层节点个数的乘积成线性关系,而与数据流规模无关,能够满足数据流分析的实时性要求.仿真结果表明,径向基统计网络能够有效解决数据流的回归和分类问题.A kind of radial basis statistical network（RBSN） over data stream is proposed based on traditional radial basis function network.The RBSN adopts a compound and multidimensional Gaussian function to fit probability densities of different areas located in input space.The learning algorithm of RBSN is designed based on a mathematical statistics method.Both the time complexity and space complexity of the network learning algorithm are linear with the product of the dimensionality of input variables and the number of hidden units,which are irrelative with the scale of data stream.Therefore,the learning algorithm of RBSN can satisfy the real-time requirement of the analysis of data stream.Simulation results show that the proposed RBSN can effectively solve both regression and classification problems over data stream.

关 键 词：数据流 径向基统计网络 回归估计 数理统计 高斯函数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]