Author:
Mahdavi-Meymand Amin,Sulisz Wojciech,Zounemat-Kermani Mohammad
Abstract
In this study novel integrative machine learning models embedded with the firefly algorithm
(FA) were developed and employed to predict energy dissipation on block ramps. The used
models include multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH), support vector regression (SVR), linear equation (LE), and nonlinear regression equation (NE). The investigation
focused on the evaluation of the performance of standard and integrative models in different
runs. The performances of machine learning models and the nonlinear equation are higher
than the linear equation. The results also show that FA increases the performance of all ap�plied models. Moreover, the results indicate that the ANFIS-FA is the most stable integrative
model in comparison to the other embedded methods and reveal that GMDH and SVR are
the most stable technique among all applied models. The results also show that the accuracy
of the LE-FA technique is relatively low, RMSE=0.091. The most accurate results provide
SVR-FA, RMSE=0.034
<b>Results</b>
<b>Conclusions</b>
<b></b>
<b></b>
Publisher
Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
Subject
Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality
Cited by
12 articles.
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