Comparative Study of Type-1 and Type-2 Fuzzy Systems Optimized by Hierarchical Genetic Algorithms

2008 ◽  
pp. 53-70 ◽  
Author(s):  
Alma I. Martinez ◽  
Oscar Castillo ◽  
Mario Garcia
Keyword(s):  
Genetic Algorithms ◽  
Comparative Study ◽  
Fuzzy Systems

scholarly journals Design of Type-1 and Interval Type-2 Fuzzy PID Control for Anesthesia Using Genetic Algorithms

2014 ◽  
Vol 06 (02) ◽  
pp. 70-93 ◽  
Author(s):  
Hugo Araujo ◽  
Bo Xiao ◽  
Chuang Liu ◽  
Yanbin Zhao ◽  
H. K. Lam
Keyword(s):  
Genetic Algorithms ◽  
Pid Control ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Interval Type

scholarly journals Comparative Study of Type-1 Fuzzy Logic and Type-2 Fuzzy Logic

2015 ◽  
Vol 124 (16) ◽  
pp. 14-21
Author(s):  
Neeru Lalka ◽  
Sushma Jain
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study

scholarly journals Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 206 ◽  
Author(s):  
Ivette Miramontes ◽  
Juan Guzman ◽  
Patricia Melin ◽  
German Prado-Arechiga
Keyword(s):  
Heart Rate ◽  
Fuzzy Systems ◽  
Membership Functions ◽  
Classification Rate ◽  
Heart Rate Level ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm ◽  
Interval Type

In this paper, the optimal designs of type-1 and interval type-2 fuzzy systems for the classification of the heart rate level are presented. The contribution of this work is a proposed approach for achieving the optimal design of interval type-2 fuzzy systems for the classification of the heart rate in patients. The fuzzy rule base was designed based on the knowledge of experts. Optimization of the membership functions of the fuzzy systems is done in order to improve the classification rate and provide a more accurate diagnosis, and for this goal the Bird Swarm Algorithm was used. Two different type-1 fuzzy systems are designed and optimized, the first one with trapezoidal membership functions and the second with Gaussian membership functions. Once the best type-1 fuzzy systems have been obtained, these are considered as a basis for designing the interval type-2 fuzzy systems, where the footprint of uncertainty was optimized to find the optimal representation of uncertainty. After performing different tests with patients and comparing the classification rate of each fuzzy system, it is concluded that fuzzy systems with Gaussian membership functions provide a better classification than those designed with trapezoidal membership functions. Additionally, tests were performed with the Crow Search Algorithm to carry out a performance comparison, with Bird Swarm Algorithm being the one with the best results.

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