The recursive neural network based nonlinear approaching ARMA model is adopted for short-term power load prediction in this paper.
本文用递归神经网络逼近非线性ARMA模型预测电力短期负荷。
The model forecasts the daily load by the nonlinear approaching capacity of the RBF neural network, than corrects the errors by on-line self-tuning factors of fuzzy control.
该模型利用RBF神经网络的非线性逼近能力对预测日负荷进行了预测，并采用在线自调整因子的模糊控制对预测误差进行在线智能修正。
On this basis, accomplishes the stage forecast for construction project quality evaluation based on the great nonlinear function approaching capability of the ANN.
在此基础上，利用人工神经网络强大的非线性函数逼近能力，实现对建筑工程质量水平的评价。
Also in this paper, an example of nonlinear function approaching is discussed and the feasibility of this method is proved.
给出了一个二维非线性函数拟合的实例，进一步验证了方法的正确性。
Also in this paper, an example of nonlinear function approaching is discussed and the feasibility of this method is proved.
给出了一个二维非线性函数拟合的实例，进一步验证了方法的正确性。
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