Journal: IEEE Access

Article Title: A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization

doi: 10.1109/access.2019.2931547

Figure Lengend Snippet: Fig. 4. Structure of ANFIS MPPT controller.

Article Snippet: MATLAB/Simulink implemented ANFIS controller interfaced with a real-time dSPACE control board that generates optimal duty ratio for SEPIC converter.

Techniques:

Journal: IEEE Access

Article Title: A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization

doi: 10.1109/access.2019.2931547

Figure Lengend Snippet: Fig. 6. Complete structure of Hybrid ANFIS-ABC control. Fig. 7. Inverter control strategy.

Article Snippet: MATLAB/Simulink implemented ANFIS controller interfaced with a real-time dSPACE control board that generates optimal duty ratio for SEPIC converter.

Techniques: Control

Journal: IEEE Access

Article Title: A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization

doi: 10.1109/access.2019.2931547

Figure Lengend Snippet: Fig. 26. Comparison of RMSE of ANFIS-ABC, ANFIS-PSO and

Article Snippet: MATLAB/Simulink implemented ANFIS controller interfaced with a real-time dSPACE control board that generates optimal duty ratio for SEPIC converter.

Techniques: Comparison

Journal: Sn Computer Science

Article Title: SimCOVID: Open-Source Simulation Programs for the COVID-19 Outbreak

doi: 10.1007/s42979-022-01441-1

Figure Lengend Snippet: A simple Simulink program used for building an ANFIS block

Article Snippet: We can also build a machine-learning program to simulate the same system using the input and output data of the model. Simulink provides the user with an adaptive neuro-fuzzy inference system (ANFIS) toolbox to generate if–then fuzzy rules automatically based on training the given data.

Techniques: Blocking Assay

Journal: Sn Computer Science

Article Title: SimCOVID: Open-Source Simulation Programs for the COVID-19 Outbreak

doi: 10.1007/s42979-022-01441-1

Figure Lengend Snippet: a Training errors for the infectious variable (left) and the cumulative infectious variable (right). b Fuzzy rules generated by ANFIS for the infectious variable (left) and the cumulative infectious variable (right). c Testing data against ANFIS output for the infectious variable (left) and the cumulative infectious variable (right)

Article Snippet: We can also build a machine-learning program to simulate the same system using the input and output data of the model. Simulink provides the user with an adaptive neuro-fuzzy inference system (ANFIS) toolbox to generate if–then fuzzy rules automatically based on training the given data.

Techniques: Generated

Journal: Sn Computer Science

Article Title: SimCOVID: Open-Source Simulation Programs for the COVID-19 Outbreak

doi: 10.1007/s42979-022-01441-1

Figure Lengend Snippet: The developed ANFIS model in Simulink

Article Snippet: We can also build a machine-learning program to simulate the same system using the input and output data of the model. Simulink provides the user with an adaptive neuro-fuzzy inference system (ANFIS) toolbox to generate if–then fuzzy rules automatically based on training the given data.

Techniques:

Journal: Sn Computer Science

Article Title: SimCOVID: Open-Source Simulation Programs for the COVID-19 Outbreak

doi: 10.1007/s42979-022-01441-1

Figure Lengend Snippet: a ANFIS outputs and the reported cases (daily on the left y-axis and cumulative on the right y-axis)—case of China. b Optimized parameters used in building the ANFIS model

Article Snippet: We can also build a machine-learning program to simulate the same system using the input and output data of the model. Simulink provides the user with an adaptive neuro-fuzzy inference system (ANFIS) toolbox to generate if–then fuzzy rules automatically based on training the given data.

Techniques: