Radial Basis Function Neural Network for Electrochemical Impedance Prediction at Presence of Corrosion Inhibitor

Abstract

Simulation and preparing predictive model of electrochemical impedance Nyquist plots based on radial basis function neural network (RBFNN) are presented in this paper. The RBFNN as a powerful predictive system predicts the real and imaginary parts of impedance as a function of time, temperature and inhibitor concentration. The mean R value of 0.9996 as regression coefficient and mean square error (MSE) value of  1.72 × 10⁻³ as results show the validity of proposed method for simulation and prediction of electrochemical impedance spectroscopy (EIS) in different environmental situations.