Predicting Fibrous Filter’s Efficiency by Two Methods: Artificial Neural Network (ANN) and Integration of Genetic Algorithm and Artificial Neural Network (GAINN)-Reference-Cited by-同舟云学术

Predicting Fibrous Filter’s Efficiency by Two Methods: Artificial Neural Network (ANN) and Integration of Genetic Algorithm and Artificial Neural Network (GAINN)

Published:2018-09-28 Issue:4 Volume:2 Page:197-205
ISSN:2510-375X
Container-title:Aerosol Science and Engineering
language:en
Short-container-title:Aerosol Sci Eng

Author:

Abdolghader Pooya,Haghighat Fariborz,Bahloul Ali^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Pollution,General Materials Science,Environmental Chemistry

Link

http://link.springer.com/article/10.1007/s41810-018-0036-2/fulltext.html

Reference39 articles.

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2. Awad WH, Herzallah R (2015) Driving licensing renewal policy using neural network-based probabilistic decision support system. Int J Comput Appl Technol 51(3):155–163

3. Bahloul A, Mahdavi A, Haghighat F, Ostiguy C (2014) Evaluation of N95 filtering facepiece respirator efficiency with cyclic and constant flows. J Occup Environ Hyg 11(8):499–508. https://doi.org/10.1080/15459624.2013.877590

4. Brochot C, Abdolghader P, Haghighat F, Bahloul A (2018) Filtration of nanoparticles applied in general ventilation. Sci Technol Built Environ. https://doi.org/10.1080/23744731.2018.1500396

5. Brown RC (1993) Air filtration: an integrated approach to the theory and applications of fibrous filters. Pergamon, New York

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