非线性时间序列的RBF神经网络预测方法及其应用  

A Method for Predicting Nonlinear Time Series Using RBF (Radial Base Function) Neural Network and Its Application

作　　者：张传斌[1] 王学孝[1] 邓正隆[1] 

机构地区：[1]哈尔滨工业大学航天学院,黑龙江哈尔滨150001

出　　处：《热能动力工程》2001年第3期311-312,342,共3页Journal of Engineering for Thermal Energy and Power

摘　　要：将一种基于自动增加隐节点数目训练算法的径向基函数 (RBF)神经网络用于非线性时间序列预测。这种方法成功地解决了BP网络的局部极小、隐节点数目的选择和过拟合问题 ,并用于热电厂热负荷预测。预测结果表明 。An innovative method revolving the use of RBF (radial base function) neural network based on a training algorithm of automatic increase in hidden nodes has been employed to predict nonlinear time series. The proposed method allows to successfully tackle the problem of selecting local minimal hidden node number and excessive fitting in BP networks. It has been applied to predict the thermal loads of a thermal power plant. The results of prediction indicate that very satisfactory results have been achieved in forecasting the thermal loads of power plants.

关 键 词：非线性时间序列 预测 RBF神经网络 热负荷 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]