| Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network | |
| Alternative Title | Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network |
| Wen Xin1; Li Xin2 | |
| 2016 | |
| Source Publication | TSINGHUA SCIENCE AND TECHNOLOGY
 |
| ISSN | 1007-0214 |
| Volume | 21Issue:5Pages:544-551 |
| Abstract | 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. |
| Other Abstract | 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. |
| Keyword | NONLINEAR-SYSTEMS STATE OBSERVER CONTROLLER FEEDBACK HYBRID Takagi-Sugeno-Kang (TSK) fuzzy model activation functions state observer nonlinear systems simulation |
| Indexed By | CSCD |
| Language | 英语 |
| CSCD ID | CSCD:5827683 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/147873 |
| Collection | 中国科学院金属研究所 |
| Affiliation | 1.中国科学院金属研究所 2.南京大学 |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment