| dc.contributor.author | Gidemen, Gökçen | |
| dc.contributor.author | Furat, Murat | |
| dc.date.accessioned | 12.07.201910:50:10 | |
| dc.date.accessioned | 2019-07-12T22:02:53Z | |
| dc.date.available | 12.07.201910:50:10 | |
| dc.date.available | 2019-07-12T22:02:53Z | |
| dc.date.issued | 2017 | |
| dc.identifier.citation | Gidemen, G., Furat, M. (2017). Parçacık Değiştirmeli PSO Algoritmas. IDAP 2017 - International Artificial Intelligence and Data Processing Symposium, art. no. 8090259.
https://doi.org/10.1109/IDAP.2017.8090259 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/IDAP.2017.8090259 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12508/494 | |
| dc.description | 2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017 -- -- 115012 | en_US |
| dc.description.abstract | Particle swarm optimization (PSO) is a swarm based optimization algorithm to find an optimum solution of a problem. In the present study, a new approach to PSO algorithm is proposed. Instead of using only the best solution at each iteration, the worst solution is added to the algorithm to enhance the performance. A PID parameter tuning problem for a second-order system having noise at the output is taken into account. Both classical and proposed algorithm are applied to the control system and performance analysis is given in graphically and statistically. The results are showed that the proposed algorithm finds the optimum solution with less particles defined in the swarm. © 2017 IEEE. | en_US |
| dc.language.iso | tur | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.isversionof | 10.1109/IDAP.2017.8090259 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Fitness function | en_US |
| dc.subject | Particle swarm optimization | en_US |
| dc.subject | PID tuning | en_US |
| dc.subject.classification | Economic Dispatch | Particle Swarm Optimizer | Global Search | en_US |
| dc.subject.other | Artificial intelligence | en_US |
| dc.subject.other | Data handling | en_US |
| dc.subject.other | Iterative methods | en_US |
| dc.subject.other | Optimization | en_US |
| dc.subject.other | Fitness functions | en_US |
| dc.subject.other | Optimization algorithms | en_US |
| dc.subject.other | Optimum solution | en_US |
| dc.subject.other | Performance analysis | en_US |
| dc.subject.other | PID parameter tuning | en_US |
| dc.subject.other | PID tuning | en_US |
| dc.subject.other | PSO algorithms | en_US |
| dc.subject.other | Second-order systemss | en_US |
| dc.subject.other | Particle swarm optimization (PSO) | en_US |
| dc.title | Parçacık değiştirmeli PSO algoritması | en_US |
| dc.title.alternative | PID Denetleyici Optimizasyonu Üzerine uygulaması | en_US |
| dc.type | conferenceObject | en_US |
| dc.relation.journal | IDAP 2017 - International Artificial Intelligence and Data Processing Symposium | en_US |
| dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- Elektrik-Elektronik Mühendisliği Bölümü | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.contributor.isteauthor | Furat, Murat | en_US |
| dc.relation.index | Scopus | en_US |
