Abstract
In this paper, an approach to design an Intuitionistic Neo-Fuzzy Network (INFN) is presented. The proposed architecture combines the advantages of the Intuitionistic Fuzzy Logic (IFL) to deal with uncertainties and the Neo-Fuzzy Neural Network approach to represent nonlinear systems with topologies including small number of parameters. As a learning approach for the consequent fuzzy rules parameters, the gradient optimization procedure is proposed. The investigate the potentials of the generated INF structure, the modeling of a three benchmark chaotic time series - Mackey-Glass, Lorenz and Rossler under uncertain conditions is investigated. The obtained results prove the flexibility of the approach and its further extension to Model Predictive Control is investigated too.
Original language | English |
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Title of host publication | 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings |
Editors | Vassil Sgurev, Ronald Yager, Mincho Hadjiski, Vladimir Jotsov |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 616-621 |
ISBN (Electronic) | 978-1-5090-1354-8, 978-1-5090-1353-1 |
ISBN (Print) | 978-1-5090-1355-5 |
DOIs | |
Publication status | Published - 7 Nov 2016 |
MoE publication type | A4 Article in a conference publication |
Event | 8th IEEE International Conference on Intelligent Systems, IS 2016 - Sofia, Bulgaria Duration: 4 Sept 2016 → 6 Sept 2016 |
Conference
Conference | 8th IEEE International Conference on Intelligent Systems, IS 2016 |
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Country/Territory | Bulgaria |
City | Sofia |
Period | 4/09/16 → 6/09/16 |
Keywords
- chaotic time series
- Intuitionistic Fuzzy Sets
- Lorenz time series
- Mackey-Glass time series
- Model Predictive Control
- Modelling
- Neo-Fuzzy Network
- Prediction
- Rossler time series