Parametric Investigation of Particle Swarm Optimization to Improve the Performance of the Adaptive Neuro-Fuzzy Inference System in Determining the Buckling Capacity of Circular Opening Steel Beams

In this paper, the main objectives are to investigate and select the most suitable parameters used in particle swarm optimization (PSO), namely the number of rules (n(rule)), population size (n(pop)), initial weight (w(ini)), personal learning coefficient (c(1)), global learning coefficient (c(2)),...

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Detalles Bibliográficos
Autores principales: Nguyen, Quang Hung, Ly, Hai-Bang, Le, Tien-Thinh, Nguyen, Thuy-Anh, Phan, Viet-Hung, Tran, Van Quan, Pham, Binh Thai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288150/
https://www.ncbi.nlm.nih.gov/pubmed/32408473
http://dx.doi.org/10.3390/ma13102210

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288150/
https://www.ncbi.nlm.nih.gov/pubmed/32408473
http://dx.doi.org/10.3390/ma13102210

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