Efficient engineering classifier capable of automatically extracting features of noise signals received

Yanning Zhang; Jincai Sun; Yulan Sun; Changxiang Ni

Efficient engineering classifier capable of automatically extracting features of noise signals received

Yanning Zhang, Jincai Sun, Yulan Sun, Changxiang Ni

School of Computer Science

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

Abstract

With the adaptive wavelet neural network and preprocessing before inputting signals into the adaptive wavelet neural network, this paper succeeded in designing and implementing an efficient engineering classifier capable of automatically extracting features of noise signals received. The efficiency of the classifier can be seen by comparing it with the neural network classifier whose feature vector is the AR parameter of signals.

Original language	English
Pages (from-to)	120-124
Number of pages	5
Journal	Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
Volume	15
Issue number	1
State	Published - 1997

Cite this

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title = "Efficient engineering classifier capable of automatically extracting features of noise signals received",

abstract = "With the adaptive wavelet neural network and preprocessing before inputting signals into the adaptive wavelet neural network, this paper succeeded in designing and implementing an efficient engineering classifier capable of automatically extracting features of noise signals received. The efficiency of the classifier can be seen by comparing it with the neural network classifier whose feature vector is the AR parameter of signals.",

author = "Yanning Zhang and Jincai Sun and Yulan Sun and Changxiang Ni",

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T1 - Efficient engineering classifier capable of automatically extracting features of noise signals received

AU - Zhang, Yanning

AU - Sun, Jincai

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AU - Ni, Changxiang

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N2 - With the adaptive wavelet neural network and preprocessing before inputting signals into the adaptive wavelet neural network, this paper succeeded in designing and implementing an efficient engineering classifier capable of automatically extracting features of noise signals received. The efficiency of the classifier can be seen by comparing it with the neural network classifier whose feature vector is the AR parameter of signals.

AB - With the adaptive wavelet neural network and preprocessing before inputting signals into the adaptive wavelet neural network, this paper succeeded in designing and implementing an efficient engineering classifier capable of automatically extracting features of noise signals received. The efficiency of the classifier can be seen by comparing it with the neural network classifier whose feature vector is the AR parameter of signals.

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M3 - 文章

AN - SCOPUS:0031387844

SN - 1000-2758

VL - 15

SP - 120

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JO - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University

JF - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University

IS - 1

ER -

Efficient engineering classifier capable of automatically extracting features of noise signals received

Abstract

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