Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices.

ABSTRACT: This study shows that the model fitting based on machine learning (ML) from experimental data can successfully predict the electrochromic characteristics of single- and dual-type flexible electrochromic devices (ECDs) by using tungsten trioxide (WO₃) and WO₃/vanadium pentoxide (V₂O₅), respectively. Seven different regression methods were used for experimental observations, which belong to single and dual ECDs where 80% percent was used as training data and the remaining was taken as testing data. Among the seven different regression methods, K-nearest neighbor (KNN) achieves the best results with higher coefficient of determination (R ²) score and lower root-mean-squared error (RMSE) for the bleaching state of ECDs. Furth

SUBMITTER: Gok EC 

PROVIDER: S-EPMC7495761 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature