| 一种改进型T-S模糊神经网络 |
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| 引用本文： | 干思权,刘贺平,申祝江.一种改进型T-S模糊神经网络[J].控制工程,2005,12(5):442-445. |
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| 作者姓名： | 干思权 刘贺平 申祝江 |
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| 作者单位： | 北京科技大学,信息工程学院,北京,100083 |
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| 摘 要： | 对T-S模糊神经网络进行了分析，提出了一种新型T-S模糊神经网络，改进了前件网络的结构及学习算法，减少了模糊规则层的节点数，有效地克服了T-S模糊神经网络模糊规则冗余的缺点。这种新型T-S模糊神经网络具有学习算法简单、收敛速度快等优点。把该网络应用到卷取温度控制中进行仿真，得到了满意的结果。
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| 关 键 词： | T-S模糊系统 模糊神经网络 非线性系统辨识 卷取温度控制 |
| 文章编号： | 1671-7848（2005）05-0442-04 |
| 收稿时间： | 2004-11-04 |
| 修稿时间： | 2004-12-07 |
An Improved T-S Fuzzy Neural Network |
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| GAN Si-quan,LIU He-ping,SHEN Zhu-jiang.An Improved T-S Fuzzy Neural Network[J].Control Engineering of China,2005,12(5):442-445. |
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| Authors: | GAN Si-quan LIU He-ping SHEN Zhu-jiang |
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| Abstract: | The T-S fuzzy neural network is analyzed, and a type of T-S fuzzy neural network is presented. The structure and learning algorithm of the antecedent network is improved. The node number of the fuzzy rule layer is reduced. The shortcoming of redtmdant fuzzy rule in the T- S fuzzy neural network is overcomed,This T-S fuzzy neural network has many advantages such as the simplicity and faster convergence speed ete, and it is applied to coiling temperature control with satisfactory results. |
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| Keywords: | T-S fuzzy system fuzzy neural network nonlinear system identification coiling temperature control |
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