Observer-based neuro identifier
| dc.contributor.author | Wen Yu | |
| dc.contributor.author | Xiaoou Li | |
| dc.contributor.author | M. Moreno | |
| dc.coverage.spatial | Bolivia | |
| dc.date.accessioned | 2026-03-22T14:59:20Z | |
| dc.date.available | 2026-03-22T14:59:20Z | |
| dc.date.issued | 2000 | |
| dc.description | Citaciones: 22 | |
| dc.description.abstract | A new online identification method is presented. The identified nonlinear systems have partial-state measurement. Their inner states, parameters and structures are unknown. The design is based on the combination of a model-free state observer and a neuro identifier. First, a sliding mode observer, which does not need any information about the nonlinear system, is applied to obtain the full states. A dynamic multilayer neural network is then used to identify the whole nonlinear system. The main contributions of the paper are: a new observer-based identification algorithm is proposed; and a stable learning algorithm for the neuro identifier is given. | |
| dc.identifier.doi | 10.1049/ip-cta:20000134 | |
| dc.identifier.uri | https://doi.org/10.1049/ip-cta:20000134 | |
| dc.identifier.uri | https://andeanlibrary.org/handle/123456789/49729 | |
| dc.language.iso | en | |
| dc.publisher | Institution of Engineering and Technology | |
| dc.relation.ispartof | IEE Proceedings - Control Theory and Applications | |
| dc.source | Center for Research and Advanced Studies of the National Polytechnic Institute | |
| dc.subject | Identifier | |
| dc.subject | Observer (physics) | |
| dc.subject | Nonlinear system | |
| dc.subject | Computer science | |
| dc.subject | State observer | |
| dc.subject | Identification (biology) | |
| dc.subject | Artificial neural network | |
| dc.subject | Control theory (sociology) | |
| dc.subject | Nonlinear system identification | |
| dc.subject | State (computer science) | |
| dc.title | Observer-based neuro identifier | |
| dc.type | article |