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Author

Mohammed Hussein Miry

Other affiliations: University of Basrah
Bio: Mohammed Hussein Miry is an academic researcher from University of Technology, Iraq. The author has contributed to research in topics: Artificial neural network & Control theory. The author has an hindex of 2, co-authored 13 publications receiving 24 citations. Previous affiliations of Mohammed Hussein Miry include University of Basrah.

Papers
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Journal ArticleDOI
TL;DR: An adaptive noise canceller algorithm based fuzzy and neural network is presented that is applied to noise canceling problem of long distance communication channel and simulation results showed that the proposed model is effectiveness.
Abstract: Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to-Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.

11 citations

Journal ArticleDOI
TL;DR: In this paper, a robust tracking controller for the automatic voltage regulator (AVR) system, where both system uncertainties and external disturbances are taken into account, is presented, and the proposed con...
Abstract: This paper presents a robust tracking controller for the automatic voltage regulator (AVR) system, where both system uncertainties and external disturbances are taken into account. The proposed con...

7 citations

Journal ArticleDOI
TL;DR: An adaptive estimator for matched and mismatched uncertainties based backstepping control is applied for DC-DC buck converter and the difference between the estimated parameters and actual parameters converges to zero.
Abstract: This paper proposed a novel adaptive robust backstepping control scheme for DC-DC buck converter subjected to external disturbance and system uncertainty. Uncertainty in the load resistance and the input voltage represent the big challenge in buck converter control. In this work, an adaptive estimator for matched and mismatched uncertainties based backstepping control is applied for DC-DC buck converter. The updating laws are determined based on the lyapunov theorem. Thus, the difference between the estimated parameters and actual parameters converges to zero. The proposed control method is compared with the conventional sliding mode control and integral sliding mode control. Simulation results demonstrate the effectiveness and robustness of the proposed controller.

6 citations

Journal ArticleDOI
TL;DR: An intelligent controller using adaptive neuro-fuzzy inference system (ANFIS) based reinforcement learning is proposed by representing the nonlinear coupled tanks system as a Markov decision process by using an algorithm based machine learning technique.
Abstract: In this paper, a novel algorithm based machine learning technique for control nonlinear coupled tanks system is presented. An intelligent controller using adaptive neuro-fuzzy inference system (ANFIS) based reinforcement learning is proposed (ANFIS-RL) by representing the nonlinear coupled tanks system as a Markov decision process. A model-free learning algorithm has been used to train a policy that controls the liquid level of the tanks system without the need to determine the dynamic model of the controlled system. Based on the optimal learned policy, which is approximated by ANFIS, the controlled system can perform the best action quickly based on the states of the system. Simulation results demonstrated the feasibility of the proposed algorithm.

4 citations

Proceedings ArticleDOI
15 Apr 2019
TL;DR: An intelligent scheme to improve the performance of the H infinity controller by selecting a suitable weighting function that ensures a robust loop shaping control that satisfies the robust stability, robust performance and provide a good tracking performance in spite of systematic uncertainties and external disturbance is presented.
Abstract: This paper presents an intelligent scheme to improve the performance of the H infinity controller by selecting a suitable weighting function that ensures a robust loop shaping control. In general, the weighting function is selected by trial and error. Recently the optimization algorithms had been developed and used widely to solve complex problems and in this paper, one of the effective optimization methods (Particle Swarm Optimization) is applied to select optimum parameters of the controller that achieves robustness response against system uncertainty with good time performance. However, a good weighting function designed by selecting a suitable objective function based on combining H infinity with H2 control schemes. Simulation results confirm that the proposed control scheme satisfies the robust stability, robust performance and provide a good tracking performance in spite of systematic uncertainties and external disturbance. Moreover, the performance of the proposed controller is compared with other control methods. The simulation results illustrated the effectiveness of the proposed control method.

4 citations


Cited by
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Journal Article
TL;DR: The identifying of the digital image in the objective function was specialized for the purpose of ensuring the image keeping accurate and the application of GA was forecasted.
Abstract: The definition,content and application of the Genetic Algorithm were mainly introduced.The identifying of the digital image in the objective function was specialized for the purpose of ensuring the image keeping accurate.The application of option,exchange and variation of operator to the GA was introduced,and the application of GA was forecasted.

84 citations

Journal ArticleDOI
TL;DR: A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation in speech as it efficiently cancelled noise even in highly noise-degraded speech.
Abstract: The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.

23 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading (PS) conditions based on a meta-heuristic approach and artificial neural network (ANN).
Abstract: To enhance the production of solar photovoltaic (SPV)-based cleaner energy, the maximum power point (MPP) tracking (MPPT) schemes are utilized. To ensure a reliable and effective MPP extraction from SPV systems, the exploitation and implementation of different MPPT schemes are of great significance. This article intends to present a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading (PS) conditions based on a meta-heuristic approach and artificial neural network (ANN). In recent years, modern optimization-based global MPP (GMPP) extraction schemes are gaining much attention from researchers. In this review article, thirteen modern optimizations and ANN-based GMPP tracking techniques are vividly described with their flowchart and detailed mathematical modeling. This work assesses all the schemes according to parameters like tracking efficacy, tracking time, application, sensed parameters, converter utilized, steady-state oscillations, experimental setup, and key notes. Based on the rigorous review, a novel GMPP extraction scheme based on a recently introduced meta-heuristic approach named artificial gorilla troops optimizer is proposed. This review work serves as a source of comprehensive information about applying these MPPT techniques to extract GMPP from the SPV system under PS conditions; furthermore, it can be considered a one-stop handbook for further study in this field.

22 citations

Journal ArticleDOI
TL;DR: The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze, and notifies authorities when there are water quality parameters out of a predefined set of normal values.
Abstract: Diseases associated with bad water have largely reported cases annually leading to deaths, therefore the water quality monitoring become necessary to provide safe water. Traditional monitoring includes manual gathering of samples from different points on the distributed site, and then testing in laboratory. This procedure has proven that it is ineffective because it is laborious, lag time and lacks online results to enhance proactive response to water pollution. Emergence of the Internet of Things (IoT) and step towards the smart life poses the successful using of IoT. This paper presents a water quality monitoring using IoT based ThingSpeak platform that provides analytic tools and visualization using MATLAB programming. The proposed model is used to test water samples using sensor fusion technique such as TDS and Turbidity, and then uploading data online to ThingSpeak platform to monitor and analyze. The system notifies authorities when there are water quality parameters out of a predefined set of normal values. A warning will be notified to user by IFTTT protocol.

19 citations

Journal ArticleDOI
TL;DR: In this paper , an adaptive backstepping method is presented for a DC-DC Buck converter utilizing a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises.
Abstract: An adaptive backstepping method is presented by this paper for a DC–DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

15 citations