| TSK动态网络及其在非线性动态系统中的应用 |
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| 引用本文： | 徐春梅,尔联洁. TSK动态网络及其在非线性动态系统中的应用[J]. 系统仿真学报, 2006, 18(8): 2358-2361,2365 |
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| 作者姓名： | 徐春梅 尔联洁 |
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| 作者单位： | 1. 北京交通大学电气工程学院,北京,100044
2. 北京航空航天大学电气工程与自动化学院,北京,100083 |
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| 摘 要： | 针对非线性动态系统特点，提出了一种新型的基于TSK模糊模型的动态回归模糊神经网DRFNN（Dynamic recurrent fuzzy neural networks），并给出了网络参教的迭代算法和基于李亚普诺夫稳定理论的收敛性证明。该动态回归网络由静态网络和内反馈动态回归网络组成，在结构上更好的拟合了非线性动态系统特点，应用于非线性动态系统的辨识和控制的试验结果也说明该动态回归模糊神经网络对解决非线性动态系统辨识和控制问题的有效性。
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| 关 键 词： | 非线性控制系统 辨识 控制 模糊神经网络 稳定性 |
| 文章编号： | 1004-731X（2006）08-2358-04 |
| 收稿时间： | 2005-04-05 |
| 修稿时间： | 2005-04-052006-06-30 |
TSK-DRFNN and Its Application in Nonlinear Dynamic Systems |
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| XU Chun-mei,ER Lian-jie. TSK-DRFNN and Its Application in Nonlinear Dynamic Systems[J]. Journal of System Simulation, 2006, 18(8): 2358-2361,2365 |
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| Authors: | XU Chun-mei ER Lian-jie |
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| Affiliation: | 1.School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China; 2.School of Automatic and Electrical Engineering, Beihang University, Beijing 100083, China |
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| Abstract: | A novel fuzzy neural networks -DRFNN based on TSK fuzzy model was proposed to nonlinear dynamic control systems. The premise and defuzification part is static networks while the consequent part is recurrent neural networks realized by llR filter. Beside the parameters, BP algorithm and convergence theory based on lyapunov methods were suggested. The structure of the proposed dynamic model is similar to that of nonlinear dynamic systems, so it can get better result when it applies to identification and control of nonlinear dynamic systems. Simulation results compared with TSK-FNN and PID were given and discussed indicting the effectiveness of the TSK-DRFNN. |
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| Keywords: | nonlinear systems control identification control fuzzy-neural networks stability |
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