Dynamic modelling of supercapacitor using artificial neural network technique

Author

Danila, Elena ; Livint, G. ; Lucache, Dorin Dumitru

Author_Institution

Dept. of Energy Utilisation, Tech. Univ. Gheorghe Asachi of Iasi, Iasi, Romania

fYear

2014

fDate

16-18 Oct. 2014

Firstpage

642

Lastpage

645

Abstract

Because of the complex dynamic behavior of supercapacitor, its modeling must be based on parallel, distributed structures (each component has to represent a model of activity, distributed on many processing units), with learning capacity. For this purpose, the paper proposes a new feed forward artificial neural network structure with two hidden layers and with backpropagation training. The neural network provides, after activation, training, testing and reinitializing, output values with a total correlation of 0, 9426 compared with target values.

Keywords

backpropagation; feedforward neural nets; power engineering computing; supercapacitors; backpropagation training; distributed structure; feedforward artificial neural network structure; hidden layer; learning capacity; parallel structure; supercapacitor dynamic modelling; Artificial neural networks; Backpropagation; Biological neural networks; Integrated circuit modeling; Neurons; Supercapacitors; Training; artificial neural network; correlation; modelling; supercapacitor;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Power Engineering (EPE), 2014 International Conference and Exposition on

Conference_Location

Iasi

Type

conf

DOI

10.1109/ICEPE.2014.6969988

Filename

6969988