| dc.creator | Ballini R. | |
| dc.creator | Gomide F. | |
| dc.date | 2002 | |
| dc.date | 2015-06-30T16:43:00Z | |
| dc.date | 2015-11-26T15:33:52Z | |
| dc.date | 2015-06-30T16:43:00Z | |
| dc.date | 2015-11-26T15:33:52Z | |
| dc.date.accessioned | 2018-03-28T22:42:29Z | |
| dc.date.available | 2018-03-28T22:42:29Z | |
| dc.identifier | | |
| dc.identifier | Ieee International Conference On Fuzzy Systems. , v. 1, n. , p. 785 - 790, 2002. | |
| dc.identifier | 10987584 | |
| dc.identifier | | |
| dc.identifier | http://www.scopus.com/inward/record.url?eid=2-s2.0-0036457989&partnerID=40&md5=46cdf7a3fc7067c94476f134875a261b | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/101721 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/101721 | |
| dc.identifier | 2-s2.0-0036457989 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1262858 | |
| dc.description | A novel recurrent, hybrid neurofuzzy network is proposed in this paper. This model is composed by two distinct parts: a fuzzy inference system and a neural network. The fuzzy system is constructed from fuzzy set models whose units of the fuzzy system are modeled through triangular norms and co-norms, and weights defined within the unit interval. The neural network contain classical nonlinear neurons. The hybrid system has a multilayer, recurrent structure. The learning procedure developed is based on two main paradigms; associative reinforcement learning and gradient search. These learning algorithms are associated to the fuzzy system and neural network, respectively. That is, output layer weights are adjusted via an error gradient method whereas a reward and punishment scheme updates the hidden layer weights. The recurrent neurofuzzy network is used to develop models of a nonlinear processes. Numerical results show that the neurofuzzy network proposed here provides accurate models after short period of learning time. | |
| dc.description | 1 | |
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| dc.description | 785 | |
| dc.description | 790 | |
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| dc.language | en | |
| dc.publisher | | |
| dc.relation | IEEE International Conference on Fuzzy Systems | |
| dc.rights | fechado | |
| dc.source | Scopus | |
| dc.title | Learning In Recurrent, Hybrid Neurofuzzy Networks | |
| dc.type | Actas de congresos | |
