Neural Networks and Pattern Recognition - Softcover

9780123995902: Neural Networks and Pattern Recognition

Softcover

ISBN 10: 0123995906 ISBN 13: 9780123995902

Publisher: Academic Press, 2013

This book is one of the most up-to-date and cutting-edge texts available on the rapidly growing application area of neural networks. Neural Networks and Pattern Recognition focuses on the use of neural networksin pattern recognition, a very important application area for neural networks technology. The contributors are widely known and highly respected researchers and practitioners in the field.

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About the Author

Omid Omidvar is a professor of Computer Science at the University of Computer Science at the University of the District of Columbia, Washington, D.C. He is also a technical director of SPPARC center; a supercomputing facility funded by NSF. He received his Ph.D. from the University of Oklahoma in 1967 and has done extensive work in applications of Neural Networks in Optical Character Recognition and Finger Print for the National Institute of Standards and Technology. Dr. Omidvar has been a consultant to many of the world's most important corporations including IBM, Sun, Gumann, and has completed a five year project for the District of Columbia NASA Consortium in design and performance evaluation of neurocontrollers. Dr. Omidvar is also the Editor-in-Chief of the Journal of Artificial Neural Networks, has been an editor of Progress in Neural Network Series since 1990, and has published a large number of journal and conference publications. In addition to teaching, Dr. Omidvar is also currently working as a computer scientist in the Image Recognition Group, Advanced System Division, at NIST.

Review

"Contributors incorporate landmark results on how neural network models have evolved from simple feedforward systems into advanced neural architectures with self-sustained activity patters, simple and complicated oscillations, specialized time elements, and new capabilities for analysis and processing of time-varying signals. Coverage includes the architecture and capabilities of pulse-coupled networks; the relationship between automata and recurrent neural networks; and a putative neurobiological model that correlates with trial-and-error learning." --REFERENCE AND RESEARCH BOOK NEWS

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