Publication:
Replication of a nonlinear dynamical system’s trajectory using the ANFIS technique
Replication of a nonlinear dynamical system’s trajectory using the ANFIS technique
| datacite.subject.fos | oecd::Natural sciences::Computer and information sciences::Computer sciences | |
| dc.contributor.author | Tri Quoc Truong | |
| dc.date.accessioned | 2022-11-09T10:35:23Z | |
| dc.date.available | 2022-11-09T10:35:23Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Machine Learning technique demonstrates various successes in the analysis of the nonlinear dynamical system. However, the limitation of previous research is that it is difficult to predict the trajectory solution for a long-time evolution. To overcome this problem, we consider a novelty approach in the Machine Learning field, named Adaptive Neuro-Fuzzy Inference System (ANFIS). By applying this method, we replicate the system’s chaotic solution based only on data collected along with time evolution. We numerically confirm the effectiveness of the ANFIS method in time series prediction. | |
| dc.identifier.doi | 10.1063/5.0066570 | |
| dc.identifier.uri | http://repository.vlu.edu.vn:443/handle/123456789/1133 | |
| dc.language.iso | en_US | |
| dc.relation.ispartof | 1ST VAN LANG INTERNATIONAL CONFERENCE ON HERITAGE AND TECHNOLOGY CONFERENCE PROCEEDING, 2021: VanLang-HeriTech, 2021 | |
| dc.relation.ispartof | AIP Conference Proceedings | |
| dc.relation.issn | 0094-243X | |
| dc.title | Replication of a nonlinear dynamical system’s trajectory using the ANFIS technique | |
| dc.type | proceedings-article | |
| dspace.entity.type | Publication |