In this paper, Ni/NiO/Ni multilayers are deposited on glass substrates using radio frequency magnetron sputtering, where the structural and morphological properties are analyzed using X-ray diffraction besides scanning electron microscopy techniques. The associated magnetic hysteresis loops are obtained by vibrating sample magnetometer for temperatures ranging from - 100 to 300 °C. Hence, the parameters α, β, Bmax, HC, and Br defining a hysteresis loop are determined at each temperature using Preisach model for the first two parameters, while the remaining ones are deduced experimentally. The set of these parameters are introduced within the training data set in the context of an ANFIS-based approach to predict the hysteresis loop of a Ni/NiO/Ni multilayer for any temperature below the Curie temperature. The comparison conducted between theoretical and experimental results showed a good agreement. This work provided more insights regarding the consolidation of experimental characterization with the development of soft computing-based frameworks.

In this paper, Ni/NiO/Ni multilayers are deposited on glass substrates using radio frequency magnetron sputtering, where the structural and morphological properties are analyzed using X-ray diffraction besides scanning electron microscopy techniques. The associated magnetic hysteresis loops are obtained by vibrating sample magnetometer for temperatures ranging from - 100 to 300 °C. Hence, the parameters α, β, Bmax, HC, and Br defining a hysteresis loop are determined at each temperature using Preisach model for the first two parameters, while the remaining ones are deduced experimentally. The set of these parameters are introduced within the training data set in the context of an ANFIS-based approach to predict the hysteresis loop of a Ni/NiO/Ni multilayer for any temperature below the Curie temperature. The comparison conducted between theoretical and experimental results showed a good agreement. This work provided more insights regarding the consolidation of experimental characterization with the development of soft computing-based frameworks.