| Autor: |
Nataliia Vlasenko, Olena Vynokurova, Alexander Vlasenko, Marta Peleshko |
| Přispěvatelé: |
Kharkiv National University of Radio Electronics, Simon Kuznets Kharkiv National University of Economics, Lviv State University of Life Safety |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). |
| DOI: |
10.1109/dsmp.2018.8478494 |
| Popis: |
In this paper we propose a hybrid five-layer neuro-fuzzy model and a corresponding learning algorithm with application in stock market time-series prediction tasks. The key difference between classical ANFIS architecture and the proposed model is in the fourth layer – multidimensional Gaussian functions are used instead of polynomials in order to achieve better computational performance and representational abilities in processing highly nonlinear volatile data. The experimental results have shown the clear advantages of the described model and its learning. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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