An explanation of how the high-speed capabilities and "learning" abilities of neural networks can be applied to solving numerous complex optimization problems in electromagnetics. It seeks to help the reader understand the basics and strengths and limitations of each main network architecture in use today. Moreover, it identifies situations when the use of neural networks is the best problem-solving option.
Auflage
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Editions-Typ
Maße
Höhe: 152 mm
Breite: 229 mm
ISBN-13
978-0-89006-880-9 (9780890068809)
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Schweitzer Klassifikation
Christos G. Christodoulou is professor and chair of the Department of Electrical and Computer Engineering at the University of New Mexico. He is co-editor of a column on wireless communications for the IEEE AP magazine. He holds a M.S. and a Ph.D. in Electrical Engineering from North Carolina State University. Michael Georgiopoulos is an associate professor at the School of Electrical Engineering and Computer Science of the University of Central Florida. He has published more than 90 papers pertaining to neural networks and holds an M.S. and a Ph.D. in Electrical Engineering from the University of Connecticut at Storrs.
Bridging neural nets and electromagnetic wave applications; single layer perceptron and multi-layer perceptron; radial basis function networks; Kohonen neural networks; ART neural network architectures; recurrent neural networks; what are considered electromagnetic applications; applications of neural networks in RF and mobile communications; applications of neural networks in scattering; applications of neural networks in radar and remote sensing; applications of neural networks in microwave circuits; applications of neural networks in computational electromagnetics. (Part contents)
