OPTIMIZATION OF THE INPUT DATA VECTOR TO IMPROVE THE NEURAL NETWORK TRAINING FOR OPC

Autor:	Almira Galeeva, Aleksey Kuzovkov, Georgiy Teplov
Rok vydání:	2021
Předmět:	Artificial neural network business.industry Computer science Training (meteorology) Artificial intelligence business Machine learning computer.software_genre computer Data vector
Zdroj:	Mathematical modeling in materials science of electronic component.
DOI:	10.29003/m2493.mmmsec-2021/137-140
Popis:	This work explored the impact of input data structure to improve the neural network training. The impact of two variants of the input data vector on the training accuracy of the network was studied. The first version of the input vector included the intensity of the exposure radiation map. The second version of the input vector included the intensity of the exposure radiation map and IC topology.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::223d5b5fbdbdc6b42e266e34ef27d4b1 https://doi.org/10.29003/m2493.mmmsec-2021/137-140 Zobrazit plný text záznamu