| 引用本文: | 任海鹏,刘 丁.一类模型未知系统的辨识和混沌化控制(英文)[J].控制理论与应用,2003,20(5):737~740.[点击复制] |
| REN Hai-peng,LIU Ding.Identification and chaotifying control of a class of system without mathematical model[J].Control Theory and Technology,2003,20(5):737~740.[点击复制] |
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| 一类模型未知系统的辨识和混沌化控制(英文) |
| Identification and chaotifying control of a class of system without mathematical model |
| 摘要点击 1619 全文点击 1234 投稿时间：2002-03-04 修订日期：2003-02-17 |
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| DOI编号 10.7641/j.issn.1000-8152.2003.5.017 |
| 2003,20(5):737-740 |
| 中文关键词 系统辨识 混沌化控制 模糊神经网络 逆系统方法 |
| 英文关键词 system identification chaotifying control fuzzy neural networks inverse system method |
| 基金项目 |
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| 中文摘要 |
| 对于一类模型未知的非混沌系统采用模糊神经网络辨识其动力学特性, 将得到的模糊神经网络辨识模型应用于逆系统方法中, 实现了一类模型未知非混沌系统的混沌化控制. 该方法不依赖于被控对象的数学模型, 就可以进行有效控制. 研究了模糊神经网络辨识误差对控制精度的影响, 证明了适当设计参数可以使由辨识误差引起的控制误差小于辨识误差. 针对连续和离散两类系统的仿真研究证明了该方法的有效性. |
| 英文摘要 |
| Fuzzy neural network (FNN) was proposed to identify the dynamics of a class of non-chaotic system without mathematical model. The result of identification was then used in inverse system method, by which chaotifying control of the system could be implemented. This method was independent of the exact mathematic model of the system to be controlled. It was testified that error of control caused by the identification error was less than the identification error by properly designed control parameters. Simulation results for continuous and discrete systems show the effectiveness of the method. |