Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90° bend

Applied Soft Computing Journal

Volumn 48, Issue , 2016, Pages 563-583

  Gholami, Azadeh ^a   Bonakdari, Hossein ^a,b   Zaji, Amir Hossein ^a   Michelson, David G ^b   Akhtari, Ali Akbar ^a  

^a RAZI UNIVERSITY   (Iran)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Decision tree; Hybrid model; Multilayer perceptron (MLP); Radial basis function (RBF); Sharp 90 bend

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; ERRORS; FORECASTING; FUNCTIONS; MEAN SQUARE ERROR; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NEURAL NETWORKS;

ARTIFICIAL INTELLIGENCE METHODS; DECISION TREE MODELING; HYBRID MODEL; HYDRAULIC CONDITIONS; MULTI LAYER PERCEPTRON; RADIAL BASIS FUNCTION MODELS; RADIAL BASIS FUNCTION(RBF); ROOT MEAN SQUARE ERRORS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 84982868496     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2016.07.035     Document Type: Article

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