Resumen
This paper presents a comparison of different machine learning techniques for classification of the unbalance and damage Niquel-Metal Hydride (Ni-MH) battery cells used in hybrid electric vehicles (HEV) and electric vehicles (EV). The implemented linear and non-linear classification algorithms used in this study are: logistic regression (LR), k-nearest neighbors (k-NN), kernel space vector machine (KSVM), Gaussian naive Bayes (GNB) and a neural network (NN); the classifiers in this work used the principal component analysis (PCA) in dual variables to reduce the high dimensional data set. To evaluate the performance of the classifiers, experimental results and a detailed analysis of the confusion matrix are used where the effectiveness of the algorithms are demonstrated.
Idioma original | Inglés |
---|---|
Título de la publicación alojada | 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 |
Editorial | Institute of Electrical and Electronics Engineers Inc. |
ISBN (versión digital) | 9781538659359 |
ISBN (versión impresa) | 9781538659359 |
DOI | |
Estado | Publicada - 5 mar. 2019 |
Evento | 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 - Ixtapa, Guerrero, México Duración: 14 nov. 2018 → 16 nov. 2018 |
Serie de la publicación
Nombre | 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 |
---|
Conferencia
Conferencia | 2018 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2018 |
---|---|
País/Territorio | México |
Ciudad | Ixtapa, Guerrero |
Período | 14/11/18 → 16/11/18 |
Nota bibliográfica
Publisher Copyright:© 2018 IEEE.