Neural networks committee decision making for estimation of metal’s hardness properties from indentation data

Autor:	I. A. Kruglov, Olga Mishulina, M. B. Bakirov
Rok vydání:	2011
Předmět:	Estimation Mathematical optimization Noise General Computer Science Artificial neural network Computer science Indentation Electrical and Electronic Engineering Perceptron Vector-valued function Electronic Optical and Magnetic Materials Quantile
Zdroj:	Optical Memory and Neural Networks. 20:132-138
ISSN:	1934-7898 1060-992X
DOI:	10.3103/s1060992x11020081
Popis:	In this paper the problem of metal's hardness properties estimation from indentation data is concerned. This problem belongs to a class of ill-posed vector function approximation problems and can't be solved by a single multilayered perceptron at the required precision level. A special neural networks committee architecture is developed in order to obtain precise estimates of metal's hardness properties. This method involves well-posed direct indentation task solution and a quantile based idea for best estimates selection. Studies have shown the estimates produced by the committee to be stable even in the case of noise presence that is similar to the true experimental one.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::650cba7a06559b7b4086be67876aaca1 https://doi.org/10.3103/s1060992x11020081 Zobrazit plný text záznamu Full text from SpringerLink