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For:	Vlasenko A, Vlasenko N, Vynokurova O, Peleshko D. A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction. Data 2018;3:62. [DOI: 10.3390/data3040062] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open

Number	Cited by Other Article(s)
1	de Campos Souza PV. Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106275] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Abstract Collapse Key Words Collapse MESH Headings Collapse Grants Collapse Affiliation(s) Collapse
2	A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting. DATA 2019. [DOI: 10.3390/data4030126] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open Abstract Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data. Collapse Key Words Collapse MESH Headings Collapse Grants Collapse Affiliation(s) Collapse