Identification and testing of an efficient Hopfield neural network magnetostriction model

Summary form only given. Magnetostriction simulation models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. In the past several phenomenological magnetostriction simulation models have been developed. However, being vectorial in nature, incorporating such models in computational packages may sometimes be inefficient from the memory allocation viewpoint. The authors present an efficient magnetostriction model based on the effective field approach in which the total applied field may be regarded as a superposition of the actual field H and a stress-dependent feedback term.