A multiresolution wavelet networks architecture and its application to pattern recognition
- Авторлар: Ejbali R.1, Jemai O.1, Zaied M.1
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Мекемелер:
- RTIM: Research Team in Intelligent Machines, National School of Engineers of Gabes
- Шығарылым: Том 27, № 3 (2017)
- Беттер: 494-510
- Бөлім: Representation, Processing, Analysis, and Understanding of Images
- URL: https://journals.rcsi.science/1054-6618/article/view/195136
- DOI: https://doi.org/10.1134/S1054661817030105
- ID: 195136
Дәйексөз келтіру
Аннотация
This paper aims at addressing a challenging research in both fields of the wavelet neural network theory and the pattern recognition. A novel architecture of the wavelet network based on the multiresolution analysis (MRWN) and a novel learning algorithm founded on the Fast Wavelet Transform (FWTLA) are proposed. FWTLA has numerous positive sides compared to the already existing algorithms. By exploiting this algorithm to learn the MRWN, we suggest a pattern recognition system (FWNPR). We show firstly its classification efficiency on many known benchmarks and then in many applications in the field of the pattern recognition. Extensive empirical experiments are performed to compare the proposed methods with other approaches.
Негізгі сөздер
Авторлар туралы
R. Ejbali
RTIM: Research Team in Intelligent Machines, National School of Engineers of Gabes
Хат алмасуға жауапты Автор.
Email: ridha_ejbali@ieee.org
Тунис, Street Omar Ibn El Khattab, Zrig Eddakhlania, Gabes, 6072
O. Jemai
RTIM: Research Team in Intelligent Machines, National School of Engineers of Gabes
Email: ridha_ejbali@ieee.org
Тунис, Street Omar Ibn El Khattab, Zrig Eddakhlania, Gabes, 6072
M. Zaied
RTIM: Research Team in Intelligent Machines, National School of Engineers of Gabes
Email: ridha_ejbali@ieee.org
Тунис, Street Omar Ibn El Khattab, Zrig Eddakhlania, Gabes, 6072
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