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.
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)
