Particle swarm optimization for fuzzy membership functions optimization

Author(s):  
A.A.A. Esmin ◽  
A.R. Aoki ◽  
G. Lambert-Torres
2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Candra Dewi ◽  
Ratna Putri P.S ◽  
Indriati Indriati

Information about the status of disease (prognosis) for patients with hepatitis is important to determine the type of action to stabilize and cure this disease. Among some system, fuzzy system is one of the methods that can be used to obtain this prognosis. In the fuzzification process, the determination of the exact range of membership function will influence the calculation of membership degree and of course will affect the final value of fuzzy system. This range and function can usually be formed using intuition or by using an algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is implemented to form the triangular membership functions in the case of patients with hepatitis. For testing process, this paper conducts four scenarios to find the best combination of PSO parameter values . Based on the testing it was found that the best parameters to form a membership function range for the hepatitis data is about 0.9, 0.1, 2, 2, 100, 500 for inertia max, inertia min, local ballast constant, global weight constant, the number of particles, and maximum iterations respectively.  


2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
M. Clement Joe Anand ◽  
Janani Bharatraj

We build a bridge between qualitative representation and quantitative representation using fuzzy qualitative trigonometry. A unit circle obtained from fuzzy qualitative representation replaces the quantitative unit circle. Namely, we have developed the concept of a qualitative unit circle from the view of fuzzy theory using Gaussian membership functions, which play a key role in shaping the fuzzy circle and help in obtaining sharper boundaries. We have also developed the trigonometric identities based on qualitative representation by defining trigonometric functions qualitatively and applied the concept to fuzzy particle swarm optimization using α-cuts.


2014 ◽  
Vol 918 ◽  
pp. 231-236
Author(s):  
Chao Lung Chiang

This paper proposes a position control of induction motor using particle swarm optimization (PSO) and fuzzy phase plane controller. Fuzzy membership functions, phase plane theory and the PSO are employed to design the proposed controller (FPPC) for controlling the position of an induction motor, based on the desired specifications. The proposed FPPC has merits of rapid response, simply designed fuzzy logic control and an explicitly designed phase plane theory. Simulations and experimental results reveal that the proposed FPPC is superior in optimal position control to conventional PI controller.


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