A Robust Signal Estimation Using Wavelet Neural Network
Publish Year: 1395
Type: Conference paper
Language: English
View: 592
This Paper With 7 Page And PDF Format Ready To Download
- Certificate
- I'm the author of the paper
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
Export:
Document National Code:
IRCEM01_162
Index date: 15 December 2016
A Robust Signal Estimation Using Wavelet Neural Network abstract
Signal Estimation has been a crucial issue in many engineering contexts including information physical and cyber security, image processing, robotics, automatic control, power system identification, and etc. This paper presents a signal prediction approach using wavelet network (WN). WN is defined as a class of neural network associated with wavelet functions that encompasses two steps of translation and dilation. To exhibit the strengths of the proposed WN, the simulation results are compared with three renowned predictors including autoregressive moving average (ARMA), recurrent neural network (RNN) and chaotic neural network (CNN). Based on the results found, the proposed WN predictor outperforms in terms of estimation signal precision and quickness of the execution time. Moreover, provided WN Network with simple and robust estimation mechanism allows one to efficiently solve other complex nonlinear signal processing problems such as system identification and adaptive equalization.
A Robust Signal Estimation Using Wavelet Neural Network Keywords:
A Robust Signal Estimation Using Wavelet Neural Network authors
Hossein Zeynal
Department of Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin
Alimorad Khajehzadeh
Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :