Open AccessNon-periodic Noisy Signals Denoising Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
Author(s) -
Imam Santoso,
Agung Warsito,
Teguh Prakoso,
Aghus Sofwan,
Ajub Ajulian Zahra,
Yuli Christyono,
Munawar Agus Riyadi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1577/1/012010
Subject(s) - noise reduction , adaptive neuro fuzzy inference system , noise (video) , gaussian noise , signal (programming language) , computer science , wavelet , pattern recognition (psychology) , discrete wavelet transform , artificial intelligence , filter (signal processing) , algorithm , mathematics , wavelet transform , fuzzy logic , computer vision , fuzzy control system , image (mathematics) , programming language
Signal always occurs with noise. Since noise acts as an unwanted signal, we must clear or reduce it with some denoising method. It is relatively easy to denoise the normal distribution noise-contaminated the periodic signal. The problem ascends if a non-Gaussian noise intrudes into a non-periodic signal. The standard filter, such as DWT (discrete wavelet transforms), cannot overcome this directly and blindly. In this research, we proposed ANFIS (Adaptive Neuro-Fuzzy Inference System) as a non-periodic noisy signal denoising method. Foremost, the ANFIS trained to mimic or estimate the interfered noise, then this noise estimation used as a subtractive signal in a non-periodic noisy signal. As a result, the ANFIS can reduce the non-Gaussian noise in the various noisy non-periodic signals with minimum error better than standard DWT (Discrete Wavelet Transform).