"Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification
| dc.contributor.author | Haznedar, Bülent | |
| dc.contributor.author | Kalınlı, Adem | |
| dc.date.accessioned | 2019-06-21T12:25:24Z | |
| dc.date.available | 2019-06-21T12:25:24Z | |
| dc.date.issued | 2016 | |
| dc.department | HKÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.description.abstract | In this study, the premise and consequent parameters of ANFIS are optimized using Genetic Algorithm (GA) based on a population algorithm. The proposed approach is applied to the nonlinear dynamic system identification problem. The simulation results of the method are compared with the Backpropagation (BP) algorithm and the results of other methods that are available in the literature. With this study it was observed that the optimisation of ANFIS parameters using GA is more successful than the other methods | en_US |
| dc.identifier.citation | Haznedar B., Kalinli A., "Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification", International Journal of Intelligent Systems and Applications in Engineering, vol.4, pp.44-47, 2016 | en_US |
| dc.identifier.doi | 10.18201/ijisae.266053 | |
| dc.identifier.endpage | 47 | en_US |
| dc.identifier.startpage | 44 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.11782/151 | |
| dc.identifier.volume | 4 | en_US |
| dc.language.iso | en | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Neuro-Fuzzy, ANFIS, Genetic Algorithm, System Identification | en_US |
| dc.title | "Training ANFIS Using Genetic Algorithm for Dynamic Systems Identification | |
| dc.type | Article |
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