Artificial Neural Networks for Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU145851" target="_blank" >RIV/00216305:26620/22:PU145851 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.wiley.com/en-it/Chemometrics+and+Numerical+Methods+in+LIBS-p-9781119759584" target="_blank" >https://www.wiley.com/en-it/Chemometrics+and+Numerical+Methods+in+LIBS-p-9781119759584</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/9781119759614.ch9" target="_blank" >10.1002/9781119759614.ch9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Neural Networks for Classification

Popis výsledku v původním jazyce

Laser‐induced plasma emission spectra contain vast amounts of information. Yet, the discovery of appropriate patterns in laser‐induced breakdown spectra is paramount to reliably performing both quantitative and qualitative analysis. This chapter provides a brief introduction of artificial neural network (ANN) classification models, which have recently become a fundamental part of most pattern recognition toolboxes. The working principles of ANNs are discussed along with the most frequently used architecture types. Special attention is given to the training process of ANNs with the aim of aiding the reader's troubleshooting capabilities. Moreover, some of the potential perils of ANN models are presented. Namely, the risk of overtraining is addressed extensively while providing several potential ailments. Lastly, a comprehensive overview of the applications of ANNs for the classification of LIBS spectra is provided and a few exemplary use‐cases of ANN classifiers are discussed in detail.

Název v anglickém jazyce

Artificial Neural Networks for Classification

Popis výsledku anglicky

Laser‐induced plasma emission spectra contain vast amounts of information. Yet, the discovery of appropriate patterns in laser‐induced breakdown spectra is paramount to reliably performing both quantitative and qualitative analysis. This chapter provides a brief introduction of artificial neural network (ANN) classification models, which have recently become a fundamental part of most pattern recognition toolboxes. The working principles of ANNs are discussed along with the most frequently used architecture types. Special attention is given to the training process of ANNs with the aim of aiding the reader's troubleshooting capabilities. Moreover, some of the potential perils of ANN models are presented. Namely, the risk of overtraining is addressed extensively while providing several potential ailments. Lastly, a comprehensive overview of the applications of ANNs for the classification of LIBS spectra is provided and a few exemplary use‐cases of ANN classifiers are discussed in detail.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10406 - Analytical chemistry

Projekt

<a href="/cs/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Kvalitní interní granty VUT</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Chemometrics and Numerical Methods in LIBS

ISBN

978-1-119-75958-4

Počet stran výsledku

28

Strana od-do

213-240

Počet stran knihy

384

Název nakladatele

Neuveden

Místo vydání

Neuveden

Kód UT WoS kapitoly

—