为了解决切换多模型预测控制的抖动问题，提出了加权多模型模糊预测控制。
To solve the problem of perturbation of witching multi-model predictive control, weighted multi-model fuzzy predictive control is put forward.
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。
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.
提出了不确定非线性系统多模型预测控制的新方法，它结合了针对非线性的基于系统输出的切换方法和针对不确定性的模型切换方法。
For the uncertain nonlinear system, the multiple-model predictive control is studied which combines the switching method for the nonlinearity and the one for the model uncertainty.
对于复杂的离散时间非线性系统，提出一种基于多模型的广义预测控制方法。
A multiple model based generalized predictive control is provided for complex nonlinear discrete time system.
针对单个BP神经网络作为预测模型时，递推多步预测误差积累大的缺点，本文提出多BP神经网络并行预测控制算法。
As the recursive error is accumulated largely when the single BP network is regarded as predictive model, we propose the paratactic predictive control algorithm based on multi-BP networks.
该算法将预测控制与多模型思想结合，通过模糊自适应加权算法计算权重，采用动态矩阵控制优化控制器参数。
The weights are calculated by fuzzy adaptive weight algorithm and the controllers are optimized by dynamic matrix control algorithm.
该算法将预测控制与多模型思想结合，通过模糊自适应加权算法计算权重，采用动态矩阵控制优化控制器参数。
The weights are calculated by fuzzy adaptive weight algorithm and the controllers are optimized by dynamic matrix control algorithm.
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