Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature-Reference-Cited by-同舟云学术

Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature

Published:2022-03-01 Issue:1 Volume:16 Page:713-723
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Zhang Guodao¹,Band Shahab S.²,Ardabili Sina³^ORCID,Chau Kwok-Wing⁴,Mosavi Amir⁵⁶⁷

Affiliation:

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, People’s Republic of China

2. Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Taiwan

3. Department of Informatics, J. Selye University, Komarom, Slovakia

4. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China

5. John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary

6. Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia

7. Institute of Information Society, University of Public Service, Budapest, Hungary

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2022.2043187

Reference23 articles.

1. Ackora-Prah, J., Sakyi, A., Ayekple, Y. E., Gyamfi, D. & Acquah, R. K. J. A. J. A. M. (2014). A bilayer feed-forward artificial neural network for exchange rate prediction.

2. Global Observational Evidence of Strong Linkage Between Dew Point Temperature and Precipitation Extremes

3. Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models

4. ANFIS application to infer student performance in English learning using affective factors

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