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Proposed Hybrid Sparse Adaptive Algorithms for System Identification
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Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS,  -RZA-LMS, p-ZA-LMS and p-RZA-LMS that are designed by merging twoconstraints from previous algorithms to improve theconvergence rate and steady state of MSD for sparse system. In this paper, a complete analysis was done for the theoretical operation of proposed algorithms by exited white Gaussian sequence for input signal. The discussion of mean square deviation (MSD) with regard to parameters of algorithms and system sparsity was observed. In addition, in this paper, the correlation between proposed algorithms and the last recent algorithms were presented and the necessary conditions of these proposed algorithms were planned to improve convergence rate. Finally, the results of simulations are compared with theoretical study (?), which is presented to match closely by a wide-range of parameters..

Keywords: Adaptive filter,  -LMS, zero-attracting, p-LMS, mean square deviation, Sparse system identification.

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Publication Date
Sun Aug 25 2019
Journal Name
Civil Engineering Journal
Optimum Efficiency of PV Panel Using Genetic Algorithms to Touch Proximate Zero Energy House (NZEH)
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By optimizing the efficiency of a modular simulation model of the PV module structure by genetic algorithm, under several weather conditions, as a portion of recognizing the ideal plan of a Near Zero Energy Household (NZEH), an ideal life cycle cost can be performed. The optimum design from combinations of NZEH-variable designs, are construction positioning, window-to-wall proportion, and glazing categories, which will help maximize the energy created by photovoltaic panels. Comprehensive simulation technique and modeling are utilized in the solar module I-V and for P-V output power. Both of them are constructed on the famous five-parameter model.  In addition, the efficiency of the PV panel is established by the genetic algorithm

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Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
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Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

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Publication Date
Mon Dec 01 2025
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Optimized stress-strain modeling of eco-friendly fiber-reinforced concrete members using meta-heuristic algorithms
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Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia

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Detection capability Alttafaria for some materials using a bacterial mutagenesis system
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Developing a system for assessing the sustainability in school building projects
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EXPERIMENTAL AND NUMERICAL ANALYSIS FOR THERMAL PROBLEM OF FRICTIONAL BRAKE SYSTEM
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USING FUZZY LOGIC CONTROLLER FOR A TWO- TANK LEVEL CONTROL SYSTEM
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This paper presents a fuzzy logic controller for a two-tank level control system, which is a process with a dead time. The fuzzy controller is a proportional-integral (PI-like) fuzzy controller which is suitable for steady state behavior of the system. Transient behavior of the system was improved without the need for a derivative action by suitable change in the rule base of the controller. Simulation results showed the step response of the two-tank level control system when this controller was used to control this plant and the effect of the dead time on the response of the system.

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Cold plasma is a relatively low temperature gas, so this feature enables us to use cold plasma to treat thermally sensitive materials including polymers and biologic tissues. In this research, the non-thermal plasma system is designed with diameter (3 mm, 10 mm) Argon at atmospheric pressure as well as to be suitable for use in medical and biotechnological applications.
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SELF ORGANIZING FUZZY CONTROLLER FOR A NON-LINEAR TIME VARYING SYSTEM
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