ON GENERALIZED ADAPTIVE NEURAL FILTERS

Zeeman Z. Zhang; Nirwan Ansari; Jean Hsang Lin

doi:10.1109/IJCNN.1992.227329

ON GENERALIZED ADAPTIVE NEURAL FILTERS

Zeeman Z. Zhang, Nirwan Ansari, Jean Hsang Lin

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

The generalized adaptive neural filter(GANF), a new class of nonlinear filters, is introduced. It is effective for non-Gaussian noise suppression. In this paper, some properties of GANF are derived, and an algorithm for finding the optimal GANF, based on the upper bound in the Minimum Absolute Error(MAE), is proposed. The implementation of the optimal GANF by using the Least Mean Square Error(LMS) and the Least Perceptron Error(LP) is also discussed. Experimental results are presented to demonstrate the effectiveness of the new filter.

Original language	English (US)
Title of host publication	Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	277-282
Number of pages	6
ISBN (Electronic)	0780305590
DOIs	https://doi.org/10.1109/IJCNN.1992.227329
State	Published - 1992
Event	1992 International Joint Conference on Neural Networks, IJCNN 1992 - Baltimore, United States Duration: Jun 7 1992 → Jun 11 1992

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	4

Conference

Conference	1992 International Joint Conference on Neural Networks, IJCNN 1992
Country/Territory	United States
City	Baltimore
Period	6/7/92 → 6/11/92

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.1992.227329

Cite this

@inproceedings{576379356f1448de9271e18265c380e7,

title = "ON GENERALIZED ADAPTIVE NEURAL FILTERS",

abstract = "The generalized adaptive neural filter(GANF), a new class of nonlinear filters, is introduced. It is effective for non-Gaussian noise suppression. In this paper, some properties of GANF are derived, and an algorithm for finding the optimal GANF, based on the upper bound in the Minimum Absolute Error(MAE), is proposed. The implementation of the optimal GANF by using the Least Mean Square Error(LMS) and the Least Perceptron Error(LP) is also discussed. Experimental results are presented to demonstrate the effectiveness of the new filter.",

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

note = "Publisher Copyright: {\textcopyright} 1992 IEEE.; 1992 International Joint Conference on Neural Networks, IJCNN 1992 ; Conference date: 07-06-1992 Through 11-06-1992",

year = "1992",

doi = "10.1109/IJCNN.1992.227329",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "277--282",

booktitle = "Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992",

address = "United States",

}

Zhang, ZZ, Ansari, N & Lin, JH 1992, ON GENERALIZED ADAPTIVE NEURAL FILTERS. in Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992. Proceedings of the International Joint Conference on Neural Networks, vol. 4, Institute of Electrical and Electronics Engineers Inc., pp. 277-282, 1992 International Joint Conference on Neural Networks, IJCNN 1992, Baltimore, United States, 6/7/92. https://doi.org/10.1109/IJCNN.1992.227329

ON GENERALIZED ADAPTIVE NEURAL FILTERS. / Zhang, Zeeman Z.; Ansari, Nirwan; Lin, Jean Hsang.
Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992. Institute of Electrical and Electronics Engineers Inc., 1992. p. 277-282 (Proceedings of the International Joint Conference on Neural Networks; Vol. 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - ON GENERALIZED ADAPTIVE NEURAL FILTERS

AU - Zhang, Zeeman Z.

AU - Ansari, Nirwan

AU - Lin, Jean Hsang

PY - 1992

Y1 - 1992

N2 - The generalized adaptive neural filter(GANF), a new class of nonlinear filters, is introduced. It is effective for non-Gaussian noise suppression. In this paper, some properties of GANF are derived, and an algorithm for finding the optimal GANF, based on the upper bound in the Minimum Absolute Error(MAE), is proposed. The implementation of the optimal GANF by using the Least Mean Square Error(LMS) and the Least Perceptron Error(LP) is also discussed. Experimental results are presented to demonstrate the effectiveness of the new filter.

AB - The generalized adaptive neural filter(GANF), a new class of nonlinear filters, is introduced. It is effective for non-Gaussian noise suppression. In this paper, some properties of GANF are derived, and an algorithm for finding the optimal GANF, based on the upper bound in the Minimum Absolute Error(MAE), is proposed. The implementation of the optimal GANF by using the Least Mean Square Error(LMS) and the Least Perceptron Error(LP) is also discussed. Experimental results are presented to demonstrate the effectiveness of the new filter.

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U2 - 10.1109/IJCNN.1992.227329

DO - 10.1109/IJCNN.1992.227329

M3 - Conference contribution

AN - SCOPUS:85024224136

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 277

EP - 282

BT - Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1992 International Joint Conference on Neural Networks, IJCNN 1992

Y2 - 7 June 1992 through 11 June 1992

ER -

ON GENERALIZED ADAPTIVE NEURAL FILTERS

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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