Improved Jiles–Atherton Model for Least Square Identification Using Sensitivity Function Normalization

We present an improved version of the Jiles-Atherton model for least square identification. We first use a simplified anhysteretic magnetization model to provide simple estimates of the initial parameters. Then we perform a normalization of sensitivity functions to improve the convergence of the Levenberg-Marquardt algorithm, leading to the emergence of optimal parameters. Experimental trials validate our method.