Mamdani and Takagi-Sugeno fuzzy classifier accuracy improvement using enhanced particle swarm optimization

2014 ◽  
Vol 26 (5) ◽  
pp. 2445-2457 ◽  
Author(s):  
Hassan M. Elragal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Accuracy Improvement ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Takagi Sugeno ◽  
Enhanced Particle Swarm Optimization

FORMATION OF FUZZY IF-THEN RULES AND MEMBERSHIP FUNCTION USING ENHANCED PARTICLE SWARM OPTIMIZATION

2013 ◽  
Vol 21 (01) ◽  
pp. 103-126 ◽  
Author(s):  
P. GANESHKUMAR ◽  
C. RANI ◽  
S. N. DEEPA
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Particle Swarm ◽  
Bench Mark ◽  
Data Sets ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Rule Set ◽  
Enhanced Particle Swarm Optimization ◽  
Test Result

This paper proposes an Enhanced Particle Swarm Optimization (EPSO) for extracting optimal rule set and tuning membership function for fuzzy logic based classifier model. The standard PSO is more sensitive to premature convergence due to lack of diversity in the swarm and can easily get trapped into local minima when it is used for data classification. To overcome this issue, BLX-α crossover and Non-uniform mutation from Genetic Algorithm (GA) are incorporated in addition to standard velocity and position updating of PSO. The performance of the proposed approach is evaluated using ten publicly available bench mark data sets. From the simulation study, it is found that the proposed approach enhances the convergence and generates a comprehensible fuzzy classifier system with high classification accuracy for all the data sets. Statistical analysis of the test result shows the suitability of the proposed method over other approaches reported in the literature.

scholarly journals Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

2020 ◽  
Vol 14 (1) ◽  
pp. 67-78
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Trial And Error Method ◽  
The Impact ◽  
Enhanced Particle Swarm Optimization ◽  
Error Method

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

An Enhanced Particle Swarm Optimization Algorithm

2009 ◽  
Vol 8 (8) ◽  
pp. 1263-1268 ◽  
Author(s):  
Xue-yao Gao ◽  
Li-quan Sun ◽  
Da-song Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Enhanced Particle Swarm Optimization

Takagi-sugeno fuzzy model identification using coevolution particle swarm optimization with multi-strategy

2016 ◽  
Vol 45 (1) ◽  
pp. 187-197 ◽  
Author(s):  
Guohan Lin ◽  
Kuiyin Zhao ◽  
Qin Wan
Keyword(s):  
Particle Swarm Optimization ◽  
Model Identification ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Swarm Optimization ◽  
Takagi Sugeno
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