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All Journal International Journal of Robotics and Control Systems Journal of Fuzzy Systems and Control (JFSC)
Aessa, Suad Ali
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Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Adaptive Fuzzy Filter Technique for Mixed Noise Removing from Sonar Images Underwater Aessa, Suad Ali; Ali, Ekbal Hussain; Shneen, Salam Waley; Abood, Layla H.
Journal of Fuzzy Systems and Control Vol. 2 No. 2 (2024): Vol. 2, No. 2, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i2.176

Abstract

Underwater Analysis of acquired images may be affected by low contrast, haze, and other disturbances., caused by scattering and absorption of the light through propagation. An adaptive fuzzy filter for three mixed noise reduction is adopted on underwater sonar images to take out the various noises that either appear in the image when captured or injected into the image when transmitted. Underwater images when captured usually have speckle noise, salt, pepper noise also Gaussian noise. Is suggested in this paper an adaptive fuzzy filter structure that combines the fuzzy filter, sigmoid sliding control to minimize error as possible, and mean filter to reduce three mixed noises from sonar images underwater. This technique gives the best results especially with speckle noise compared to mean filter, median filter as no adaptive filters, and fuzzy filters, frost filter as adaptive filters. The MATLAB programs are adopted to simulate the proposed system.
Reduction of Large Scale Linear Dynamic MIMO Systems Using Adaptive Network Based Fuzzy Inference System Oudah, Manal Kadhim; Shneen, Salam Waley; Aessa, Suad Ali
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1684

Abstract

Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.
Co-Authors Abood, Layla H. Ali, Ekbal Hussain Oudah, Manal Kadhim Salam Waley Shneen