A Fractional-Order Dynamic Photovoltaic Model Parameters Estimation Based on Chaotic Meta-Heuristic Optimization Algorithms

Author(s):  
Dalia Yousri ◽  
Dalia Allam ◽  
M. B. Eteiba
2019 ◽  
Vol 95 (3) ◽  
pp. 2491-2542 ◽  
Author(s):  
D. A. Yousri ◽  
Amr M. AbdelAty ◽  
Lobna A. Said ◽  
A. S. Elwakil ◽  
Brent Maundy ◽  
...  

2021 ◽  
Author(s):  
Mohamed S. Ghoneim ◽  
Samar I. Ismail ◽  
Lobna A. Said ◽  
Ahmed M. Eltawil ◽  
Ahmed G. Radwan ◽  
...  

Abstract Bio-impedance non-invasive measurement techniques usage is rapidly increasing in the agriculture industry. These measured impedance variations reflect tacit biochemical and biophysical changes of living and non-living tissues. Bio-impedance circuit modeling is an effective solution used in biology and medicine to fit the measured impedance. This paper proposes two new fractional-order bio-impedance plant stem models. These new models are compared among three commonly used bio-impedance fractional-order circuit models in plant modeling (Cole, Double Cole, FO Double-shell). The two proposed models represent the characterization of the biological cellular morphology of the plant stem through a non-invasive method. Experiments are conducted on two samples of three different medical plant species under the family Lamiaceae, and each sample is measured at two inter-electrode spacing distances. Bio-impedance measurements are done using an electrochemical station (SP150) in the frequency range from 100 Hz to 100 kHz. All employed models are compared by fitting the measured data to find the most suitable circuit model that models the plant stem. The proposed models give the best results in all inter-electrode spacing distances. Four different meta-heuristic optimization algorithms are used in the fitting process to extract all models parameter and find the best optimization algorithm in the bio-impedance problems.


2021 ◽  
Vol 35 (4) ◽  
pp. 1149-1166
Author(s):  
Hossien Riahi-Madvar ◽  
Majid Dehghani ◽  
Rasoul Memarzadeh ◽  
Bahram Gharabaghi

Author(s):  
WITOLD PAWLUS ◽  
HAMID REZA KARIMI

In this paper a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system is modeled and simulated. Two well-known phenomenological hysteresis models are explored: Bouc–Wen and Dahl ones. In particular, influence of their parameters on the response is evaluated and assessed. The next step is to introduce the artificial neural networks and discuss their application in the field of systems identification. Subsequently, two feedforward neural networks are created and trained to estimate parameters characterizing each of the MR damper models described. The semi-active suspension (SAS) system equipped with a MR brake is described and the detailed procedure for acquisition of the reference data used in the models validation stage is elaborated. The models outputs obtained by simulating them with the values of coefficients as identified by the networks are compared to each other as well as to the reference experimental data. Thanks to that, the comparative analysis between the suggested vibration suppression models and the full-scale MR brake is done and it is concluded which of the discussed models has a better performance. The usability of neural networks in the field of parameters estimation of the mathematical models of the real world phenomena is described as well. The novelty of the presented methodology is the application of artificial intelligence methods to estimate model parameters of a MR brake utilized in a SAS system. The results of this approach have a strong potential to be successfully implemented in the area of model-based control of semi-active vibration suppression systems.


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