scholarly journals An Interval Type-2 Adaptive Neuro-Fuzzy Inference System Based, Artificial Pacemaker Design and Stability Analysis

2022 ◽  
Author(s):  
Asghar Dabiri ◽  
Nader Jafarnia Dabanloo ◽  
Fereidoun Nooshiravan ◽  
Keivan Maghooli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Square ◽  
Step Response ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Interval Type ◽  
System File

Abstract 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.

scholarly journals Interval Type-2 Fuzzy Inference System Based on Closest Point of Approach for Collision Avoidance between Ships

2020 ◽  
Vol 10 (11) ◽  
pp. 3919 ◽  
Author(s):  
Sung Wook Ohn ◽  
Ho Namgung
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Collision Risk ◽  
Inference System ◽  
Interval Type

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.

scholarly journals Missing Samples Estimation of Synthetic ECG Signals by FCM-based Adaptive Neuro-Fuzzy Inference System (FCMANFIS)

2021 ◽  
Author(s):  
asghar dabiri ◽  
Nader Jafarnia Dabanloo ◽  
Fereidoon Nooshirvan Rahatabad ◽  
Keivan Maghooli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Synthetic Data ◽  
Least Square ◽  
Inference System ◽  
Ecg Signals ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
System File

Abstract 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

Sign in / Sign up

Export Citation Format

Share Document