Title :
Artificial neural networks in the solution of inverse electromagnetic field problems
Author :
Hoole, S. Ratnajeevan H
Author_Institution :
Harvey Mudd Coll., Claremont, CA, USA
fDate :
3/1/1993 12:00:00 AM
Abstract :
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. Particularly if one knows the desired geometry approximately and uses training sets around this geometry, simple neural networks with a few training sets can be used to do an effective job. However, neural networks cannot be used efficiently without such prior knowledge
Keywords :
electrical engineering computing; electromagnetic fields; neural nets; artificial neural networks; geometry; inverse electromagnetic field problems; prior knowledge; training sets; well-trained networks; Artificial neural networks; Biological neural networks; Computer networks; Educational institutions; Electromagnetic devices; Electromagnetic fields; Finite element methods; Intelligent networks; Inverse problems; Neurons;
Journal_Title :
Magnetics, IEEE Transactions on