A Novel Analog Modulation Classification: Discrete Wavelet Transform-Extreme Learning Machine (DWT-ELM)
Yükleniyor...
Dosyalar
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Bitlis Eren Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The aim of this study is to propose a method using discrete wavelet transform and extreme learning machine (DWT-ELM) in classification of communication signals. Six types of analog modulated signals as “AM”, “DSB”, “USB”, “LSB”, “FM” and “PM” are used for classification and analog modulated signal dataset consists of 1920 signals. These signals are also added white noise. Feature extraction is performed using different DWT filters. The feature vector obtained from DWT is used in classification. ELM is preferred due to its advantages over conventional back-propagation based classification. The feature vector is fed by the inputs of the ELM. The performance of the proposed method is evaluated for different types of DWT filters. In addition, compared results with similar study are presented to be able to determine the success of the proposed method.
Açıklama
Anahtar Kelimeler
DWT-ELM, ELM classification, Wavelet Transform, Analog modulated signals
Kaynak
Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
10
Sayı
2
Künye
USTUNDAG, M. A Novel Analog Modulation Classification: Discrete Wavelet Transform-Extreme Learning Machine (DWT-ELM). Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 10(2), 492-506.
