摘要
为提高复杂工业系统非线性时间序列预测精度,将工业系统非线性时间序列不同的单个预测模型预测值作为函数链神经网络的原始输入值,并将原始输入值按正交的三角函数扩展得到的数值作为函数链神经网络扩展输入值,在分析函数链神经网络拟合充要条件的基础上,结合模糊自适应变权重算法计算函数链神经网络权重,建立基于模糊自适应变权重算法的函数链神经网络预测模型。研究结果表明:基于模糊自适应变权重算法的函数链神经网络预测方法的预测精度较高,并且平均误差和预测平方根误差均较小,具有较强的泛化能力;该模糊自适应变权重函数链神经网络预测模型可用于复杂非线性工业系统决策。
In order to enhance forecasting precision problem about nonlinear time series in complex industry system,a functional link neural network forecasting model was established based on fuzzy adaptive variable weight algorithm by using of making some forecasting values from different single forecasting models of nonlinear time series in complex industry system as original input values of functional link neural network,making the original input values as patulous input values of functional link neural network after the original input values being extended according to orthogonal trigonometric function,analyzing necessary and sufficient conditions of functional link neural network fitting and calculating the weight of functional link neural network based on fuzzy adaptive variable weight algorithm.The simulation analysis results and forecasting results of the severe harm rate in some mine reveal that the functional link neural network forecasting method based on fuzzy adaptive variable weight algorithm has higher accuracy than that of every single combined forecasting model or other forecasting model.The functional link neural network forecasting is of good extensive capability and is very useful for requirement decision in complex industry system.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第9期2812-2818,共7页
Journal of Central South University:Science and Technology
基金
国家"十一五"科技支撑计划项目(2007BAK22B04-12
2006BAB02B05-01-02-01)
关键词
函数链神经网络
模糊自适应变权重算法
预测
模糊
神经网络
functional link neural network
fuzzy adaptive variable weight algorithm
forecast
fuzzy
neural network
