Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function

2019 ◽  
Vol 36 (6) ◽  
Author(s):  
Mohammad Mahdi Zabihi Shesh Poli ◽  
Mahdi Aliyari Shoorehdeli ◽  
Ali Moarefianpour
Keyword(s):  
Stability Analysis ◽  
Lyapunov Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Interval Type

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.

Sign in / Sign up

Export Citation Format

Share Document