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Complex Valued Nonlinear Adaptive Filters
Complex Valued Nonlinear Adaptive Filters
Knygos.lt klubas Knygos.lt nariams
192,65 €
-35%
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296,39 €
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This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex sta…
  • Leidėjas:
  • ISBN-10: 0470066350
  • ISBN-13: 9780470066355
  • Formatas: 17.3 x 24.9 x 2.5 cm, kieti viršeliai
  • Kalba: Anglų

Complex Valued Nonlinear Adaptive Filters (el. knyga) (skaityta knyga) | knygos.lt

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This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.

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  • Autorius: Danilo P Mandic
  • Leidėjas:
  • ISBN-10: 0470066350
  • ISBN-13: 9780470066355
  • Formatas: 17.3 x 24.9 x 2.5 cm, kieti viršeliai
  • Kalba: Anglų

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.

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