Control and diagnostic of the complex impedance of selected perovskite compounds

In this work the diagnosis control of the complex impedance of selected perovskite compounds versus artificial neural network model optimized with the Levenberg–Marquardt algorithm is performed as detection of aging and degradation of materials usually requires destructive testing. The artificial ne...

Full description

Saved in:
Bibliographic Details
Published in:Optical and quantum electronics 2017-10, Vol.49 (10), p.1-14, Article 337
Main Authors: Tarbi, Amal, Atmani, E. H., Sellam, M. A.
Format: Article
Language:English
Subjects:
Aging (artificial)
Aging (materials)
Artificial neural networks
Characterization and Evaluation of Materials
Computer Communication Networks
Destructive testing
Diagnostic systems
Electrical Engineering
Grain boundaries
Impedance
Lasers
Mathematical models
Neural networks
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
Robustness (mathematics)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work the diagnosis control of the complex impedance of selected perovskite compounds versus artificial neural network model optimized with the Levenberg–Marquardt algorithm is performed as detection of aging and degradation of materials usually requires destructive testing. The artificial neural network optimized by the Levenberg–Marquardt algorithm used in this work allows us to monitor the materials (LaNd) SrMnCrO 3 in a non-destructive manner. This intelligent control is done by calculating the complex impedance which reveals reliable information on the phenomenon of transport in materials. The method overcomes the problem of the lack of a mathematical expression between the input parameters (temperature, doping, and frequency) and the necessary parameters for computing the impedance (bulk resistance, grain boundary resistance, and the two parameters of the constant phase element impedance A 0 and P). The robustness and performance of the artificial neural network model was verified by introducing additional noise and by using the root mean square error and the R-square.
ISSN:0306-8919
1572-817X
DOI:10.1007/s11082-017-1173-6