Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2 | Zendy

Amin Bemani | Zendy; Alireza Baghban | Zendy; Shahaboddin Shamshirband | Zendy; Amir Mosavi | Zendy; Péter Csiba | Zendy; Annamária R. Várkonyi-Kóczy | Zendy

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Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

Author(s) -

Amin Bemani,

Alireza Baghban,

Shahaboddin Shamshirband,

Amir Mosavi,

Péter Csiba,

Annamária R. Várkonyi-Kóczy

Publication year - 2020

Publication title -

computers, materials and continua/computers, materials and continua (print)

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.788

H-Index - 40

eISSN - 1546-2226

pISSN - 1546-2218

DOI - 10.32604/cmc.2020.07723

Subject(s) - supercritical carbon dioxide , solubility , adaptive neuro fuzzy inference system , artificial neural network , least squares support vector machine , perceptron , multilayer perceptron , supercritical fluid , artificial intelligence , support vector machine , computer science , machine learning , fuzzy logic , radial basis function , extreme learning machine , biological system , chemistry , fuzzy control system , organic chemistry , biology

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