Publications

2016
Bencer S, Boudoukha A, Mouni L. Multivariate statistical analysis of the groundwater of Ain Djacer area (Eastern of Algeria). Arabian Journal of GeosciencesArabian Journal of Geosciences. 2016;9 :1-10.
Bencer S, Boudoukha A, Mouni L. Multivariate statistical analysis of the groundwater of Ain Djacer area (Eastern of Algeria). Arabian Journal of GeosciencesArabian Journal of Geosciences. 2016;9 :1-10.
Benkherbache S, Mohamed SI-A. Natural convection in a cylindrical and divergent annular duct fitted with fins. International Journal of Energy, Environment and EconomicsInternational Journal of Energy, Environment and Economics. 2016;24 :503-518.
Benkherbache S, Mohamed SI-A. Natural convection in a cylindrical and divergent annular duct fitted with fins. International Journal of Energy, Environment and EconomicsInternational Journal of Energy, Environment and Economics. 2016;24 :503-518.
ALLOUI Z, Vasseur P. Natural convection in tall and shallow porous rectangular enclosures heated from below. Computational Thermal Sciences: An International JournalComputational Thermal Sciences: An International Journal. 2016;8.
ALLOUI Z, Vasseur P. Natural convection in tall and shallow porous rectangular enclosures heated from below. Computational Thermal Sciences: An International JournalComputational Thermal Sciences: An International Journal. 2016;8.
Laib H, CHAGHI ABDELAZIZ, Wira P. A Neural and Fuzzy Logic Based Control Scheme for a Shunt Active Power Filter. International Conference on Electrical Engineering and Control Applications. 2016 :201-211.
Laib H, CHAGHI ABDELAZIZ, Wira P. A Neural and Fuzzy Logic Based Control Scheme for a Shunt Active Power Filter. International Conference on Electrical Engineering and Control Applications. 2016 :201-211.
Laib H, CHAGHI ABDELAZIZ, Wira P. A Neural and Fuzzy Logic Based Control Scheme for a Shunt Active Power Filter. International Conference on Electrical Engineering and Control Applications. 2016 :201-211.
Makhloufi T-M, Abdessemed Y, Khireddine M-S. A neural network MPP tracker using a Buck-Boost DC/DC converter for photovoltaic systems. 5th International Conference on Systems and Control (ICSC) [Internet]. 2016. Publisher's VersionAbstract

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

Khireddine S-M, Abdessemed Y, Makhloufi M-T. A neural network MPP tracker using a Buck-Boost DC/DC converter for photovoltaic systems. 5th International Conference on Systems and Control (ICSC) [Internet]. 2016. Publisher's VersionAbstract

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

Makhloufi T-M, Abdessemed Y, Khireddine M-S. A neural network MPP tracker using a Buck-Boost DC/DC converter for photovoltaic systems. 5th International Conference on Systems and Control (ICSC) [Internet]. 2016. Publisher's VersionAbstract

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

Khireddine S-M, Abdessemed Y, Makhloufi M-T. A neural network MPP tracker using a Buck-Boost DC/DC converter for photovoltaic systems. 5th International Conference on Systems and Control (ICSC) [Internet]. 2016. Publisher's VersionAbstract

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

Makhloufi T-M, Abdessemed Y, Khireddine M-S. A neural network MPP tracker using a Buck-Boost DC/DC converter for photovoltaic systems. 5th International Conference on Systems and Control (ICSC) [Internet]. 2016. Publisher's VersionAbstract

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

Khireddine S-M, Abdessemed Y, Makhloufi M-T. A neural network MPP tracker using a Buck-Boost DC/DC converter for photovoltaic systems. 5th International Conference on Systems and Control (ICSC) [Internet]. 2016. Publisher's VersionAbstract

This paper proposes an artificial neural network (ANN) controller for the maximum power point tracking (MPPT) of a photovoltaic system under rapidly varying temperature and solar radiation conditions. This intelligent control method is applied to a DC/DC Buck-Boost converter. The main difference between the proposed systems to existing MPPT control systems is that it includes an automatic determination of the main switch duty cycle which permits an optimal operation of the control circuit under steady and perturbed environmental conditions. The maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and it estimates the optimum duty cycle corresponding to maximum power as output. The different steps of the design of the intelligent controller are presented hereby with some simulation results using Matlab/Simulink software.

Bentouhami L, Abdessemed R, BENDJEDDOU YACINE, Merabet E. Neuro-fuzzy control of a dual star induction machine. Journal of Electrical EngineeringJOURNAL OF ELECTRICAL ENGINEERING. 2016;16.
Bentouhami L, Abdessemed R, BENDJEDDOU YACINE, Merabet E. Neuro-fuzzy control of a dual star induction machine. Journal of Electrical EngineeringJOURNAL OF ELECTRICAL ENGINEERING. 2016;16.
Bentouhami L, Abdessemed R, BENDJEDDOU YACINE, Merabet E. Neuro-fuzzy control of a dual star induction machine. Journal of Electrical EngineeringJOURNAL OF ELECTRICAL ENGINEERING. 2016;16.
Bentouhami L, Abdessemed R, BENDJEDDOU YACINE, Merabet E. Neuro-fuzzy control of a dual star induction machine. Journal of Electrical EngineeringJOURNAL OF ELECTRICAL ENGINEERING. 2016;16.
Djeffal EA, Djeffal L, Benoumelaz F. New Complexity Analysis of the Path Following Method for Linear Complementarity Problem. In: Intelligent Mathematics II: Applied Mathematics and Approximation Theory. Springer ; 2016. pp. 87-104.

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