nonlinear approaching ability 非线性逼近能力
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。
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