Full Product Details
Author: Omid Omidvar (University of the District of Columbia) ,
Judith Dayhoff (Institute of System Research, University of Maryland) ,
Omid Omidvar (University of the District of Columbia) ,
Omid Omidvar (University of the District of Columbia)
Publisher: Elsevier Science Publishing Co Inc
Imprint: Academic Press Inc
Dimensions:
Width: 15.20cm
, Height: 2.30cm
, Length: 22.90cm
Weight: 0.660kg
ISBN: 9780125264204
ISBN 10: 0125264208
Pages: 351
Publication Date: 29 October 1997
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Out of Print
Availability: In Print
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.
Reviews
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 & RESEARCH BOOK NEWS
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 & RESEARCH BOOK NEWS
""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 & RESEARCH BOOK NEWS
Author Information
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.
