Computational modeling and simulation has developed and expanded into a diverse range of fields such as digital signal processing, image processing, robotics, systems biology, and many more; enhancing the need for a diversifying problem solving applications in this area. Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Maße
Höhe: 286 mm
Breite: 221 mm
Dicke: 25 mm
Gewicht
ISBN-13
978-1-4666-3942-3 (9781466639423)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
<strong>Boris Igelnik</strong> received MS degree in electrical engineering from the Moscow Electrical Engineering Institute of Communication, MS degree in mathematics from the Moscow State University, and PhD degree in Electrical Engineering from the Institute for Problems of Information Transmission, Academy of Sciences USSR, Moscow, Russia and the Moscow Electrical Engineering Institute of Communication. He is Chief Scientist at the BMI Research, Inc., Richmond Heights (Cleveland), OH, USA. His current research interests are in the areas of computational and artificial intelligence, digital signal processing, image processing, adaptive control, robotics, and computational models of intellect. Boris Igelnik is a Senior Member of IEEE.
<strong>Jacek M. Zurada</strong> received his MS and PhD degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. He is Director of Computational Intelligence Lab, University of Louisville, Louisville, KY, USA. His current research interests are in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits. He is a Member of Polish Academy of Sciences and Fellow of IEEE. Dr. Zurada has been elected IEEE VP Technical Activities 2014.
