Structure and properties of generalized adaptive neural filters for signal enhancement

Zeeman Z. Zhang; Nirwan Ansari

doi:10.1109/72.508929

Structure and properties of generalized adaptive neural filters for signal enhancement

Zeeman Z. Zhang, Nirwan Ansari

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	857-868
Number of pages	12
Journal	IEEE Transactions on Neural Networks
Volume	7
Issue number	4
DOIs	https://doi.org/10.1109/72.508929
State	Published - 1996

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/72.508929

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title = "Structure and properties of generalized adaptive neural filters for signal enhancement",

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.",

author = "Zhang, {Zeeman Z.} and Nirwan Ansari",

note = "Funding Information: Manuscript received August 9, 1994; revised March 9, 1995, September 10, 1995, and Noveinber 19, 1995. This work was supported in part by the New Jersey Commi!ision on Science and Technology under Contract IP96-2890-040- 18. Z. Z. Zhang was with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102 USA. He is now with the Science and Technology Division, BellSouth, Atlanta, GA 30375 IJSA. N. Ansari is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102 USA. Publisher Item Identifier S 1045-9227(96)02875-5.",

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T1 - Structure and properties of generalized adaptive neural filters for signal enhancement

AU - Zhang, Zeeman Z.

AU - Ansari, Nirwan

N1 - Funding Information: Manuscript received August 9, 1994; revised March 9, 1995, September 10, 1995, and Noveinber 19, 1995. This work was supported in part by the New Jersey Commi!ision on Science and Technology under Contract IP96-2890-040- 18. Z. Z. Zhang was with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102 USA. He is now with the Science and Technology Division, BellSouth, Atlanta, GA 30375 IJSA. N. Ansari is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102 USA. Publisher Item Identifier S 1045-9227(96)02875-5.

PY - 1996

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N2 - 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.

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Structure and properties of generalized adaptive neural filters for signal enhancement

Abstract

All Science Journal Classification (ASJC) codes

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