Architectures and algorithms for nonlinear adaptive filters

V. Hegde, C. Radhakrishnan, D. J. Krusienski, W. K. Jenkins

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper considers series-cascade nonlinear adaptive filter architectures consisting of a linear input filter, a memoryless polynomial nonlinearity, and a linear output filter (LNL). The learning characteristics of the LNL structure are studied in terms of performance and complexity. Replacing the linear input stage and the memoryless nonlinear stage of the LNL model with a Volterra module is then considered. Adaptive algorithms are summarized for these structures and experimental examples are used to illustrate performance for the identification of an acoustic echo channel.

Original languageEnglish (US)
Pages (from-to)1015-1016
Number of pages2
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
StatePublished - 2002

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications


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