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 neural network optimized by the Levenberg–Marquardt algorithm used in this work allows us to monitor the materials (LaNd) SrMnCrO₃ 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 A₀ 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.