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Conventional and Inverse QRD-RLS Algorithms

  • José A. ApolinárioJrEmail author
  • Maria D. Miranda
Chapter

Abstract

This chapter deals with the basic concepts used in the recursive least-squares (RLS) algorithms employing conventional and inverse QR decomposition. The methods of triangularizing the input data matrix and the meaning of the internal variables of these algorithms are emphasized in order to provide details of their most important relations. The notation and variables used herein will be exactly the same used in the previous introductory chapter. For clarity, all derivations will be carried out using real variables and the final presentation of the algorithms (tables and pseudo-codes) will correspond to their complex-valued versions.

Keywords

Systolic Array Cholesky Factor Plane Rotation Error Energy Optimum Estimation Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Military Institute of Engineering (IME)Rio de JaneiroBrazil
  2. 2.University of São Paulo (USP)São PauloBrazil

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