Comparative study on the generalized adaptive neural filter with other nonlinear filters

  • Authors:

  • Henry Hanek;Nirwan Ansari;Zeeman Z. Zhang

  • Affiliations:

  • Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, New Jersey;Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, New Jersey;Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, New Jersey

  • Venue:
  • ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
  • Year:
  • 1993

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Abstract

The Generalized Adaptive Neural Filter (GANF) is a new type of adaptable filter. The GANF relies upon neural functions to set up a filtering operation. This paper looks at a few of the possible neural operators which can be used in a GANF. The capabilities of the neural nets are examined and the filtering abilities of the GANF are obtained through simulation. While the GANF structure used here is somewhat simplified, the filter is also compared to other non-adaptive filters. These filters provide a reference so that relative performance can be more realistically judged.