Structure and properties of generalized adaptive neural filters for signal enhancement

Zeeman Z. Zhang, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

This article addresses the structure and properties of a new class of nonlinear adaptive filters called generalized adaptive neural filters (GANF's). Various properties, such as an upper bound of the mean absolute error of the filters, are analytically derived. Experimental results are presented to demonstrate the performance of the filters for signal and image enhancement. It is shown that GANF's not only extend the class of stack filters, but also have better performance in noise suppression.

Original languageEnglish (US)
Pages (from-to)857-868
Number of pages12
JournalIEEE Transactions on Neural Networks
Volume7
Issue number4
DOIs
StatePublished - 1996

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Structure and properties of generalized adaptive neural filters for signal enhancement'. Together they form a unique fingerprint.

Cite this