基于混合优化的RBF神经网络模型研究  

Research on an Optimized RBF Neural Network Model Applied to CPI Forecast

作　　者：罗芳琼[1] 

机构地区：[1]柳州师范高等专科学校数学与计算机科学系,广西柳州545004

出　　处：《计算机与现代化》2013年第7期51-55,共5页Computer and Modernization

基　　金：广西教育厅科研基金资助项目(201204LX506);柳州师范高等专科学校科研基金资助项目(LSZ2011B007)

摘　　要：根据我国居民消费价格指数的非线性特征及RBF神经网络参数难以确定的问题,建立基于正交最小二乘(OLS)、K-均值聚类、梯度下降法相结合,激活函数为高斯函数、反常S型函数和拟多二次函数的线性组合形式的混合优化RBF神经网络模型,同时运用所建模型对我国居民消费价格指数进行拟合和预测。实验结果表明,该模型能够很好地解决居民消费价格指数拟合和预测这一问题,预测精度比单独使用一种算法和基于高斯函数的混合算法都高,具有一定的普遍适用性。This paper,aiming at the nonlinear characteristics of CPI and the parameters of being difficult to be objectively determined in RBF neural network,puts forward a kind of optimization of RBF neural network method which combines orthogonal least squares（OLS）,K-means clustering and gradient descent algorithm,computing activation function with the linear combinations of Gauss,reflected sigmoidal and inverse multiquadrics radial basis functions,then builds a model for CPI to fit and forecast by using the optimization algorithm of RBF neural network.Experimental results show that the model is of a good convergence and generalization ability,the model has a certain universal applicability in the prediction performance which is obviously superior to the single method forecast and the hybrid network on Gauss kernel function.

关 键 词：径向基神经网络 优化混合算法 居民消费价格指数预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]