@inproceedings{69e98f515641487db2c99a671ce945a1,
title = "Exploiting OSC models by using neural networks with an innovative pruning algorithm",
abstract = "In this paper we have investigated the relationship between the current and the active layer thickness of an organic solar cell (OSC) in order to improve its efficiency by means of a back propagation neural network. In order to preserve the generalization properties of the adopted neural network (NN) in this paper is presented also an innovative pruning technique. The extensive simulations performed show a good agreement between simulated and experimental data with an overall error of about 3%. The obtained results demostrate that the use of an MLP with associated an appropriate pruning algorithm to preserve its generalization capacities permits to accurately reproduce the relationship between the active layer thicknesses and the measured maximum power in an OSC. This neural model can be of great use in manufacturing processes.",
author = "{Lo Sciuto}, Grazia and Giacomo Capizzi and Christian Napoli and Rafi Shikler and Dawid Po{\l}ap and Marcin Wo{\'z}niak",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018 ; Conference date: 03-06-2018 Through 07-06-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-91262-2_62",
language = "English",
isbn = "9783319912615",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "711--722",
editor = "Ryszard Tadeusiewicz and Leszek Rutkowski and Witold Pedrycz and Rafal Scherer and Marcin Korytkowski and Zurada, {Jacek M.}",
booktitle = "Artificial Intelligence and Soft Computing - 17th International Conference, ICAISC 2018, Proceedings",
address = "Germany",
}
