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基于RBF神经网络的粮库温度预测
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2009-08-04
贾桂林
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由于粮库温度是非线性的时间序列，文章提出了基于RBF神经网络的粮库温度预测模
型。该模型优于传统的粮库温度分析方法，又避免了BP算法容易陷入局部极小点和收敛速度慢的缺点。根据实验的仿真结果显示，该模型对于粮库温度的预测效果较好。
关键词：粮库温度，径向基函数神经网络，非线性时间序列
Abstract: Because of the grain depot temperature is a nonlinear time series, this paper presents the grain depot temperature forecast model based on tradition basic function neural networks. The model is not only better than the tradition stock technical analysis method but also is avoiding the defects which relapse into the partial smallest point and convergence rate is little of BP algorithm. The simulation results of the experiment show that model is efficient to forecast the trend of grain depot temperature.
Keywords: Grain depot temperature, RBF neural network，nonlinear time series
型。该模型优于传统的粮库温度分析方法，又避免了BP算法容易陷入局部极小点和收敛速度慢的缺点。根据实验的仿真结果显示，该模型对于粮库温度的预测效果较好。
关键词：粮库温度，径向基函数神经网络，非线性时间序列
Abstract: Because of the grain depot temperature is a nonlinear time series, this paper presents the grain depot temperature forecast model based on tradition basic function neural networks. The model is not only better than the tradition stock technical analysis method but also is avoiding the defects which relapse into the partial smallest point and convergence rate is little of BP algorithm. The simulation results of the experiment show that model is efficient to forecast the trend of grain depot temperature.
Keywords: Grain depot temperature, RBF neural network，nonlinear time series
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