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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hou Zhi-xiang Li He-qing |
| Copyright Year | 2006 |
| Description | Author affiliation: Coll. of Automobile & Mech. Eng., Changsha Univ. of Sci. & Technol. (Hou Zhi-xiang) |
| Abstract | System identification is the basis of designing control system, and it is very difficulty to identify the nonlinear system today. A new identification method of nonlinear system based adaptive neural fuzzy inference system (ANFIS) is provided in this paper, which assembles the advantages of fuzzy theory and neural networks. The structure and algorithms of ANFIS is introduced firstly, then a nonlinear function is tested using the method, and the simulation results show that ANFIS is very effective to identify the nonlinear system |
| Sponsorship | IEEE Circuits and Syst. Soc. HKUST UESTC City Univ. of Hong Kong |
| Starting Page | 2067 |
| Ending Page | 2069 |
| File Size | 2766691 |
| Page Count | 3 |
| File Format | |
| ISBN | 0780395840 |
| DOI | 10.1109/ICCCAS.2006.285085 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-06-25 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Assembly systems Neural networks Nonlinear control systems Fuzzy neural networks Control systems Inference algorithms System identification Nonlinear systems Fuzzy systems |
| Content Type | Text |
| Resource Type | Article |
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