A Novel Method for Multi-Targets ISAR Imaging Based on Particle Swarm Optimization and Modified CLEAN Technique

2016 ◽  
Vol 16 (1) ◽  
pp. 97-108 ◽  
Author(s):  
Lei Liu ◽  
Feng Zhou ◽  
Mingliang Tao ◽  
Zijing Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Isar Imaging ◽  
Novel Method

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces

2012 ◽  
Vol 239-240 ◽  
pp. 1027-1032 ◽  
Author(s):  
Qing Guo Wei ◽  
Yan Mei Wang ◽  
Zong Wu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Channel Selection ◽  
L1 Norm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Channel Reduction ◽  
Novel Method

Applying many electrodes is undesirable for real-life brain-computer interface (BCI) application since the recording preparation can be troublesome and time-consuming. Multi-objective particle swarm optimization (MOPSO) has been widely utilized to solve multi-objective optimization problems and thus can be employed for channel selection. This paper presented a novel method named cultural-based MOPSO (CMOPSO) for channel selection in motor imagery based BCI. The CMOPSO method introduces a cultural framework to adapt the personalized flight parameters of the mutated particles. A comparison between the proposed algorithm and typical L1-norm algorithm was conducted, and the results showed that the proposed approach is more effective in selecting a smaller subset of channels while maintaining the classification accuracy unreduced.

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