Nonlinearity Correction in Dynamic Measuring Devices Using Neural Network Models

Публікація:
Nonlinearity Correction in Dynamic Measuring Devices Using Neural Network Models

dc.contributor.author	Ravashdeh, L. A. M.
dc.contributor.author	Zakharov, I. P.
dc.contributor.author	Zaporozhets, O. V.
dc.date.accessioned	2022-01-03T20:48:16Z
dc.date.available	2022-01-03T20:48:16Z
dc.date.issued	2020
dc.description.abstract	A neural network compensator for the nonlinearity of a dynamic measuring instrument is proposed, which allows restoring the value of the measured input signal. The inverse model of a nonlinear dynamic measuring device is implemented based on a three-layer perceptron supplemented by delay lines of input signals. The properties of the proposed neural network compensator are studied through simulation computer modelling using various types of calibration input signals for the training of an artificial neural network.	uk_UA
dc.identifier.citation	Ravashdeh L. A. M. Nonlinearity Correction in Dynamic Measuring Devices Using Neural Network Models / L.A.M. Ravashdeh, I. Zakharov, O. Zaporozhets // Pomiary Automatica Robotyka, 2020, №4. - P. 57-60	uk_UA
dc.identifier.issn	1427-9126
dc.identifier.uri	https://openarchive.nure.ua/handle/document/18977
dc.language.iso	en	uk_UA
dc.relation.ispartofseries	PAR;2020, №4, pp. 57-60
dc.subject	artificial neural network	uk_UA
dc.subject	three-layer perceptron	uk_UA
dc.subject	training	uk_UA
dc.subject	inverse model	uk_UA
dc.subject	neural network compensator	uk_UA
dc.title	Nonlinearity Correction in Dynamic Measuring Devices Using Neural Network Models	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication