Detection of Ferro-Metallic Objects from Magnetic Data Using a Scaled Hopfield Neural Network

The purpose of the present paper is to investigate the Hopfield neural network as a flexible detection method of Ferro-magnetic sources from magnetic anomaly data. The observed magnetic anomaly is approximated over a steel drum by an equivalent dipole source. The Hopfield network was used to obtain the magnetic moment at a set of regular locations. For each location, the Hopfield network reaches its stable state and the location of minimum Hopfield energy signifies the target location. To escape from the local minimum of the network energy function, the scaled input technique is proposed.