To handle the unsymmetrical dynamic characteristics of some nonlinear systems in chemical process, a multi-model predictive control method was proposed based on self-tuning model.
针对化工过程某些非线性系统的不对称动态特性，提出了一种基于自校正模型的多模型预测控制算法。
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。
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。
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