Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network
文献类型：期刊论文
| 作者 | Wen Xin1; Li Xin2 |
| 刊名 | TSINGHUA SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2016 |
| 卷号 | 21期号:5页码:544-551 |
| 关键词 | NONLINEAR-SYSTEMS STATE OBSERVER CONTROLLER FEEDBACK HYBRID Takagi-Sugeno-Kang (TSK) fuzzy model activation functions state observer nonlinear systems simulation |
| ISSN号 | 1007-0214 |
| 其他题名 | Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network |
| 英文摘要 | In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network (SRCPFWNN) for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network (SRWNN) to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang (TSK) model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory (DACT) and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system. |
| 语种 | 英语 |
| CSCD记录号 | CSCD:5827683 |
| 源URL | [http://ir.imr.ac.cn/handle/321006/147872]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 作者单位 | 1.中国科学院金属研究所 2.南京大学 |
| 推荐引用方式 GB/T 7714 | Wen Xin,Li Xin. Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network[J]. TSINGHUA SCIENCE AND TECHNOLOGY,2016,21(5):544-551. |
| APA | Wen Xin,&Li Xin.(2016).Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network.TSINGHUA SCIENCE AND TECHNOLOGY,21(5),544-551. |
| MLA | Wen Xin,et al."Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network".TSINGHUA SCIENCE AND TECHNOLOGY 21.5(2016):544-551. |
入库方式： OAI收割
来源：金属研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。