The Minimum Gradient Complexity Control Applied to Sensitivity Extraction of Electromagnetic Devices

This paper applies the parallel layer perceptron network trained with the minimum gradient method (PLP-MGM) to the problem of indirect sensitivity extraction of electromagnetic devices. The networks trained with the MGM are less dependent of users´ defined parameters, as, for instance, the number of neurons. Some results are presented considering the indirect sensitivity extraction of a loudspeaker magnet assembly unit and an inverse scattering problem, and they show the effectiveness of the proposed approach.