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A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability
(John Wiley and Sons, 2022) Çolak, Andaç Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Akhtar, Md Tanwir; Shafiq, Anum
This study focuses on accurately predicting the behavior of new power functiondistribution using neural network and optimizing it using maximum likelihoodestimation. The main motivation of this study is that there is no study inthe literature that optimizes and predicts the reliability analysis of lifetimemodels by combining artificial neural networks and maximum likelihoodestimation methods. The numerical findings of the reliability investigationsand the values got from maximum likelihood estimation and artificial neuralnetwork modeling have been examined and investigated carefully. For theartificial neural network models, the R value was 0.99999 and the deviationratios were lower than 0.08%. The findings reveal that artificial neural networksare a powerful and useful mathematical tool for analyzing the reliabilityof lifetime models and numerical study findings via maximum likelihoodestimation are completely in accord with artificial neural network predictionresults.