ARTIFICIAL NEURAL NETWORKS IN THE SOLUTION OF INVERSE ELECTROMAGNETIC-FIELD PROBLEMS

In an inverse electromagnetic field problem, we are asked to determine those values of the descriptive parameters of the device that would give us a required performance. In this paper, the use of artificial neural networks in the solution of inverse electromagnetic field problems is investigated. It is shown that artificial neural networks, while being no panacea, have a role to play in a limited domain of applications. That is, while it is ineffective to train networks to cover a broad class of devices, it is indeed possible to develop well-trained networks that function effectively over a narrow range of performance of a particular class of device.