论文成果
基于Hammerstein-Wiener模型的连续搅拌反应釜神经网络预测控制
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- 发表时间：2011-01-01
- 发表刊物：化工学报
- 文献类型：J
- 期号：8
- 页面范围：2275-2280
- ISSN号：0438-1157
- 摘要：A model predictive control strategy based on neural network is presented for a continuous stirred tank reactor(CSTR). A segmentation method was adopted to identify Hammerstein-Wiener model coefficient by least squares support vector machines and then to construct a nonlinear predictive controller which was by a linear optimal component and radial basis function neural networks in series. A nonlinear predictive control algorithm based on least support vector machines Hammerstein-Wiener model was realized by using BP neural network to train predictive input sequences and to solve nonlinear predictive control rules by Quasi-Newton method. The simulation results of CSTR illustrate that this approach is effective tracking and controlling product concentration. © All Rights Reserved.
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