Paper Info
Title
Nature-inspired metaheuristic multivariate adaptive regression splines for predicting refrigeration system performance
Abstract
This study aims to build an artificial intelligence (AI)-based inference model to predict the coefficient of performance (COP) for refrigeration equipment under various R404A refrigerant conditions. The proposed model, the evolutionary multivariate adaptive regression splines (EMARS), is a hybrid of the multivariate adaptive regression splines (MARS) and the artificial bee colony (ABC). In the EMARS, the MARS primarily addresses the learning and curve fitting and the ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. A tenfold cross-validation method was used to compare the performance of the EMARS against four other AI techniques, including the back-propagation neural network, classification and regression tree, genetic programming, and support vector machine. An analysis of comparison results supports EMARS as the best model for predicting the COP, with an MAPE value $$
Year
DOI
Venue
2017
10.1007/s00500-015-1798-y
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Keywords
Field
DocType
Multivariate adaptive regression splines, Coefficient of performance, Artificial bee colony, Refrigeration system, Energy consumption
Multivariate adaptive regression splines,Decision tree,Mathematical optimization,Curve fitting,Computer science,Inference,Support vector machine,Genetic programming,Artificial intelligence,Artificial neural network,Machine learning,Metaheuristic
Journal
Volume
Issue
ISSN
21
2
1432-7643
Citations 
PageRank 
References 
0
0.34
16
Authors
3
Name
Order
Citations
PageRank
Min-Yuan Cheng117419.84
Jui-Sheng Chou214917.95
Minh-Tu Cao3313.13