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Neural Networks: A Comprehensive Foundation
From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, itExpand
  • 24,440
  • 2220
Adaptive filter theory
Background and Overview. 1. Stochastic Processes and Models. 2. Wiener Filters. 3. Linear Prediction. 4. Method of Steepest Descent. 5. Least-Mean-Square Adaptive Filters. 6. NormalizedExpand
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GradientBased Learning Applied to Document Recognition
Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture,Expand
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Cognitive radio: brain-empowered wireless communications
  • S. Haykin
  • Computer Science
  • IEEE Journal on Selected Areas in Communications
  • 7 February 2005
Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio,Expand
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Neural Networks and Learning Machines
  • 4,235
  • 460
Cubature Kalman Filters
In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named the cubature Kalman filter (CKF). The heart of the CKF is a spherical-radial cubature rule,Expand
  • 1,690
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Adaptive Filter Theory 4th Edition
  • 1,420
  • 269
Kalman Filtering and Neural Networks
From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. ThisExpand
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  • 162
Adaptive filter theory (2nd ed.)
  • 1,839
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Adaptive Filter Theory" Third Edition
  • 1,005
  • 151