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.
|Titolo:||Exploiting OSC models by using neural networks with an innovative pruning algorithm|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|