Asghar Dabiri - Academia.edu (original) (raw)

Papers by Asghar Dabiri

Research paper thumbnail of Design and Frequency Stability Analysis of an Adaptive Neuro-Fuzzy Inference System based Artificial Pacemaker Controller in MATLAB

Research paper thumbnail of An Interval Type-2 Adaptive Neuro-Fuzzy Inference System based, Artificial Pacemaker Design and Stability Analysis

Journal of Long-term Effects of Medical Implants, 2023

This paper presents the design and simulation of an Interval type 2 fuzzy system (IT2FS) based, a... more This paper presents the design and simulation of an Interval type 2 fuzzy system (IT2FS) based, adaptive neuro-fuzzy inference system (ANFIS) pacemaker controller in MATLAB. After designing the type 1 fuzzy logic model, the stability of the designed system has been verified in the time-domain (unit step response). In previous works, the type 1 (IT1FS) model step response was analyzed. They are compared with the other proportional integral derivative (PID) and fuzzy models that only least-square-estimation and the backpropagation algorithms are used for tuning membership functions (MF) and generation of type 1 fis (fuzzy inference system) file. At current work, fuzzy C means (FCM) method shows better results than other methods have been used. The pacemaker controller determines the pacing rate and adjusts the heart rate of the patient for the reference input signal. The rise-time, overshoot and settling-time have been improved significantly.

Research paper thumbnail of Design and Stability Analysis of an Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Pacemaker controller in MATLAB Simulink

Research Square (Research Square), Apr 5, 2023

This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system... more This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system-based controller of a pacemaker in MATLAB Simulink. ANFIS uses Learning and Speed properties of Fuzzy and Neural Networks. Based on body states and preprogrammed situations of patients (age and sex, etc.),heart rate and amplitude of pacing pulse are changed. Output signal that is fed backed from heart is compared to the reference fuzzy bases ANFIS signals .After designing ANFIS based controller, the stability of the proposed system has been tested in both Time (Step response) and Frequency domains(Bode Diagram and Nichols chart). In our previous paper Step response analyzed and compared with other works. For frequency domain, all the possible frequency analysis methods have been tested but because of nonlinear properties of ANFIS, after linearization, just the Bode diagram achieved good results. The step response results in time domain is compared with previous work's results including optimum heart pulse rate for each particular patient. In frequency-domain the Bode diagram stability analysis showed Gain and phase margin as follows: GM (dB)= 42.1 and PM (deg) = 100

Research paper thumbnail of Design and Stability Analysis of an Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Pacemaker controller in MATLAB Simulink

This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system... more This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system-based controller of a pacemaker in MATLAB Simulink. ANFIS uses Learning and Speed properties of Fuzzy and Neural Networks. Based on body states and preprogrammed situations of patients (age and sex, etc.),heart rate and amplitude of pacing pulse are changed. Output signal that is fed backed from heart is compared to the reference fuzzy bases ANFIS signals .After designing ANFIS based controller, the stability of the proposed system has been tested in both Time (Step response) and Frequency domains(Bode Diagram and Nichols chart). In our previous paper Step response analyzed and compared with other works. For frequency domain, all the possible frequency analysis methods have been tested but because of nonlinear properties of ANFIS, after linearization, just the Bode diagram achieved good results. The step response results in time domain is compared with previous work's results includ...

Research paper thumbnail of Missing Samples Estimation of Synthetic ECG Signals by FCM-based Adaptive Neuro-Fuzzy Inference System (FCMANFIS)

This paper presents estimation of missed samples recovery of Synthetic electrocardiography (ECG) ... more This paper presents estimation of missed samples recovery of Synthetic electrocardiography (ECG) signals by an ANFIS (Adaptive neuro-fuzzy inference system) method. After designing the ANFIS model using FCM (Fuzzy C Means) clustering method. In MATLAB’s standard library for ANFIS, only least-square-estimation and the back-propagation algorithms are used for tuning membership functions and generation of fis (fuzzy inference system) file, but at current work we have used FCM method that shows better result. Root mean square error (difference of the reference input and the generated data by ANFIS) for the three synthetic data cases are: a. Train data: RMSE = 1.7112e-5b. Test data: RMSE = 5.184e-3c. All data: RMSE = 2.2663e-3

Research paper thumbnail of An Interval Type-2 Adaptive Neuro-Fuzzy Inference System Based, Artificial Pacemaker Design and Stability Analysis

This paper presents design and simulation of an Interval type-2 fuzzy system (IT2FS) based, Adapt... more This paper presents design and simulation of an Interval type-2 fuzzy system (IT2FS) based, Adaptive neuro-fuzzy inference system(ANFIS) pacemaker controller in MATLAB. After designing the type-1 fuzzy logic model, the stability of the designed system has been verified in the time-domain (unit step response). In previous works, type-1 (IT1FS) model step response was analyzed and compared with the other PID and Fuzzy models that only least-square-estimation and the backpropagation algorithms are used for tuning membership functions and generation of type-1 fis (fuzzy inference system) file, but at current work Fuzzy C Means (FCM) method that shows better results has been used. The pacemaker controller determines the pacing rate and adjusts the heart rate of the patient with respect to the reference input signal. The rise-time, overshoot and settling-time have been improved significantly.

Research paper thumbnail of Design and Frequency Stability Analysis of an Adaptive Neuro-Fuzzy Inference System based Artificial Pacemaker Controller in MATLAB

Research paper thumbnail of An Interval Type-2 Adaptive Neuro-Fuzzy Inference System based, Artificial Pacemaker Design and Stability Analysis

Journal of Long-term Effects of Medical Implants, 2023

This paper presents the design and simulation of an Interval type 2 fuzzy system (IT2FS) based, a... more This paper presents the design and simulation of an Interval type 2 fuzzy system (IT2FS) based, adaptive neuro-fuzzy inference system (ANFIS) pacemaker controller in MATLAB. After designing the type 1 fuzzy logic model, the stability of the designed system has been verified in the time-domain (unit step response). In previous works, the type 1 (IT1FS) model step response was analyzed. They are compared with the other proportional integral derivative (PID) and fuzzy models that only least-square-estimation and the backpropagation algorithms are used for tuning membership functions (MF) and generation of type 1 fis (fuzzy inference system) file. At current work, fuzzy C means (FCM) method shows better results than other methods have been used. The pacemaker controller determines the pacing rate and adjusts the heart rate of the patient for the reference input signal. The rise-time, overshoot and settling-time have been improved significantly.

Research paper thumbnail of Design and Stability Analysis of an Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Pacemaker controller in MATLAB Simulink

Research Square (Research Square), Apr 5, 2023

This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system... more This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system-based controller of a pacemaker in MATLAB Simulink. ANFIS uses Learning and Speed properties of Fuzzy and Neural Networks. Based on body states and preprogrammed situations of patients (age and sex, etc.),heart rate and amplitude of pacing pulse are changed. Output signal that is fed backed from heart is compared to the reference fuzzy bases ANFIS signals .After designing ANFIS based controller, the stability of the proposed system has been tested in both Time (Step response) and Frequency domains(Bode Diagram and Nichols chart). In our previous paper Step response analyzed and compared with other works. For frequency domain, all the possible frequency analysis methods have been tested but because of nonlinear properties of ANFIS, after linearization, just the Bode diagram achieved good results. The step response results in time domain is compared with previous work's results including optimum heart pulse rate for each particular patient. In frequency-domain the Bode diagram stability analysis showed Gain and phase margin as follows: GM (dB)= 42.1 and PM (deg) = 100

Research paper thumbnail of Design and Stability Analysis of an Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Pacemaker controller in MATLAB Simulink

This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system... more This paper presents the design and stability analysis of an Adaptive neuro-fuzzy inference system-based controller of a pacemaker in MATLAB Simulink. ANFIS uses Learning and Speed properties of Fuzzy and Neural Networks. Based on body states and preprogrammed situations of patients (age and sex, etc.),heart rate and amplitude of pacing pulse are changed. Output signal that is fed backed from heart is compared to the reference fuzzy bases ANFIS signals .After designing ANFIS based controller, the stability of the proposed system has been tested in both Time (Step response) and Frequency domains(Bode Diagram and Nichols chart). In our previous paper Step response analyzed and compared with other works. For frequency domain, all the possible frequency analysis methods have been tested but because of nonlinear properties of ANFIS, after linearization, just the Bode diagram achieved good results. The step response results in time domain is compared with previous work's results includ...

Research paper thumbnail of Missing Samples Estimation of Synthetic ECG Signals by FCM-based Adaptive Neuro-Fuzzy Inference System (FCMANFIS)

This paper presents estimation of missed samples recovery of Synthetic electrocardiography (ECG) ... more This paper presents estimation of missed samples recovery of Synthetic electrocardiography (ECG) signals by an ANFIS (Adaptive neuro-fuzzy inference system) method. After designing the ANFIS model using FCM (Fuzzy C Means) clustering method. In MATLAB’s standard library for ANFIS, only least-square-estimation and the back-propagation algorithms are used for tuning membership functions and generation of fis (fuzzy inference system) file, but at current work we have used FCM method that shows better result. Root mean square error (difference of the reference input and the generated data by ANFIS) for the three synthetic data cases are: a. Train data: RMSE = 1.7112e-5b. Test data: RMSE = 5.184e-3c. All data: RMSE = 2.2663e-3

Research paper thumbnail of An Interval Type-2 Adaptive Neuro-Fuzzy Inference System Based, Artificial Pacemaker Design and Stability Analysis

This paper presents design and simulation of an Interval type-2 fuzzy system (IT2FS) based, Adapt... more This paper presents design and simulation of an Interval type-2 fuzzy system (IT2FS) based, Adaptive neuro-fuzzy inference system(ANFIS) pacemaker controller in MATLAB. After designing the type-1 fuzzy logic model, the stability of the designed system has been verified in the time-domain (unit step response). In previous works, type-1 (IT1FS) model step response was analyzed and compared with the other PID and Fuzzy models that only least-square-estimation and the backpropagation algorithms are used for tuning membership functions and generation of type-1 fis (fuzzy inference system) file, but at current work Fuzzy C Means (FCM) method that shows better results has been used. The pacemaker controller determines the pacing rate and adjusts the heart rate of the patient with respect to the reference input signal. The rise-time, overshoot and settling-time have been improved significantly.