| dc.date.accessioned | 2020-03-11T20:30:26Z | |
| dc.date.accessioned | 2022-10-18T22:49:02Z | |
| dc.date.available | 2020-03-11T20:30:26Z | |
| dc.date.available | 2022-10-18T22:49:02Z | |
| dc.date.created | 2020-03-11T20:30:26Z | |
| dc.date.issued | 2014 | |
| dc.identifier | http://hdl.handle.net/10533/239100 | |
| dc.identifier | 15110019 | |
| dc.identifier | WOS:000331545600007 | |
| dc.identifier | no scielo | |
| dc.identifier | eid=2-s2.0-84893150308 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4470439 | |
| dc.description.abstract | In this paper a class of hybrid-fuzzy models is presented, where binary membership functions are used to capture the hybrid behavior. We describe a hybrid-fuzzy identification methodology for non-linear hybrid systems with mixed continuous and discrete st | |
| dc.language | eng | |
| dc.relation | https://doi.org/10.1016/j.asoc.2013.12.011 | |
| dc.relation | 10.1016/j.asoc.2013.12.011 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.title | Hybrid-fuzzy modeling and identification | |
| dc.type | Articulo | |
