Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability

Authors:
Danilo P. Mandic;Jonathon Chambers
Affiliations:
-;-
Venue:
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Year:
2001

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Abstract

From the Publisher:From mobile communications to robotics to space technology to medical instrumentation, new technologies are demanding increasingly complex methods of digital signal processing (DSP). This book shows researchers how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques. Featuring original research on stability in neural networks, the book combines rigorous mathematical analysis with application examples. Experimental evidence as well as an overview of existing approaches are also included. Market: Engineers working in signal processing, neural networks, communications, nonlinear control, and time series analysis.