RBF neural network regression model based on fuzzy observations  

基于模糊观测数据的RBF神经网络回归模型(英文)

作　　者：朱红霞[1,2] 沈炯[1] 苏志刚[1] 

机构地区：[1]东南大学能源与环境学院,南京210096 [2]南京工程学院能源与动力工程学院,南京211167

出　　处：《Journal of Southeast University(English Edition)》2013年第4期400-406,共7页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.51106025,51106027,51036002);Specialized Research Fund for the Doctoral Program of Higher Education(No.20130092110061);the Youth Foundation of Nanjing Institute of Technology(No.QKJA201303)

摘　　要：A fuzzy observations-based radial basis function neural network （FORBFNN） is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization （ EM ） algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.提出了一种基于模糊观测数据的RBF神经网络(FORBFNN),用于解决一类输出不可精确测量但可用模糊隶属度来表征的非线性系统建模问题.神经网络模型中各隐层神经单元的权重系数采用一种新的模糊EM算法辨识获得;隐层神经单元的数量及径向基函数的中心和宽度基于一种数据驱动的方法自适应确定,即首先初始生成一个隐层单元,然后根据一定的规则逐步加入新的单元,该过程不断迭代直到模型满足预设要求.该方法同时考虑了模型的复杂度及预测精度.数值模拟实验结果表明该建模方法是有效的,且建立的模型具有较高的预测精度.

关 键 词：radial basis function neural network （RBFNN） fuzzy membership function imprecise observation regression model 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]