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Complex valued nonlinear adaptive filters : noncircularity, widely linear and neural models /

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 fee...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mandic, Danilo P.
Otros Autores: Goh, Vanessa Su Lee
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Chichester, U.K. : Wiley, 2009.
Colección:Adaptive and learning systems for signal processing, communications, and control.
Temas:
Acceso en línea:Texto completo
Tabla de Contenidos:
  • The magic of complex numbers
  • Why signal processing in the complex domain?
  • Adaptive filtering architectures
  • Complex nonlinear activation functions
  • Elements of CR calculus
  • Complex valued adaptive filters
  • Adaptive filters with feedback
  • Filters with an adaptive stepsize
  • Filters with an adaptive amplitude of nonlinearity
  • Data-reusing algorithms for complex valued adaptive filters
  • Complex mappings and Möbius transformations
  • Augmented complex statistics
  • Widely linear estimation and augmented CLMS (ACLMS)
  • Duality between complex valued and real valued filters
  • Widely linear filters with feedback
  • Collaborative adaptive filtering
  • Adaptive filtering based on EMD
  • Validation of complex representations : is this worthwhile?
  • Some distinctive properties of calculus in C
  • Liouville's theorem
  • Hypercomplex and Clifford algebras
  • Real valued activation functions
  • Elementary transcendental functions (ETF)
  • The O notation and standard vector and matrix differentiation
  • Notions from learning theory
  • Notions from approximation theory
  • Terminology used in the field of neural networks
  • Complex valued pipelined recurrent neural network (CPRNN)
  • Gradient adaptive step size (GASS) algorithms in R
  • Derivation of partial derivatives from Chapter 8
  • A posteriori leraning
  • Notions from stability theory
  • Linear relaxation
  • Contraction mappings, fixed point iteration, and fractals.