scholarly journals Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

2016 ◽  
Vol 21 (sup1) ◽  
pp. 132-141 ◽  
Author(s):  
Mi Li ◽  
Xiaofeng Lu ◽  
Xiaodong Wang ◽  
Shengfu Lu ◽  
Ning Zhong
Keyword(s):  
Particle Swarm Optimization ◽  
Information Entropy ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Swarm Optimization ◽  
Kernel Svm

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Parameter Identification Method for Turbine Speed Governor System Based on Particle Swarm Optimization

2013 ◽  
Vol 448-453 ◽  
pp. 2511-2515
Author(s):  
Wen Sun ◽  
Xiang Yu Kong ◽  
Qun Yang ◽  
Fang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Evolutionary Strategy ◽  
Identification System ◽  
Swarm Optimization ◽  
Identification Method ◽  
Speed Governor ◽  
Speed Governor System

A parameter identification method for generator speed governor system, which combines decoupling parameter identification and overall recognition with measured data, was proposed in the paper. The method bases on particle swarm optimization, and takes parameter identification as a parameters optimization problem under evaluation function. According to an intelligent optimization algorithms evolutionary strategy, the individual's status is continuously adjusted until the identification system and actual system output error is sufficiently small. Case studies show that the proposed method can be applied to the measured parameters and model validation work.

scholarly journals Turning Parameters Optimization Using Particle Swarm Optimization

2014 ◽  
Vol 69 ◽  
pp. 670-677 ◽  
Author(s):  
Hrelja Marko ◽  
Klancnik Simon ◽  
Irgolic Tomaz ◽  
Paulic Matej ◽  
Balic Joze ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Swarm Optimization

Double-Loop PID Parameter Tuning for DC Motor Based on Modified Particle Swarm Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3139-3142
Author(s):  
Tao Cheng ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
High Performance ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Parameters Optimization ◽  
Double Loop ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

This paper proposes a modified particle swarm optimization to solve identification of tuning PID controller parameters. This paper elaborates the process that MPSO algorithm optimizes PID parameters in double-loop speed control system modeled by simulink. Through analyzing the results of the MPSO optimization, and comparing with standard PSO(SPSO) and traditional method, MPSO algorithm has better dynamic performance, provides a high performance methods for PID parameters optimization.

Cutting Parameters Optimization by Using Particle Swarm Optimization (PSO)

2007 ◽  
Vol 10-12 ◽  
pp. 879-883 ◽  
Author(s):  
Jian Guang Li ◽  
Ying Xue Yao ◽  
Dong Gao ◽  
Chang Qing Liu ◽  
Zhe Jun Yuan
Keyword(s):  
Particle Swarm Optimization ◽  
Essential Role ◽  
Particle Swarm ◽  
Cutting Parameters ◽  
Fundamental Principle ◽  
Parameters Optimization ◽  
Machining Process ◽  
Swarm Optimization ◽  
Cutting Parameters Optimization ◽  
Cutting Experiments

Cutting parameters play an essential role in the economics of machining. In this paper, particle swarm optimization (PSO), a novel optimization algorithm for cutting parameters optimization (CPO), was discussed comprehensively. First, the fundamental principle of PSO was introduced; then, the algorithm for PSO application in cutting parameters optimization was developed; thirdly, cutting experiments without and with optimized cutting parameters were conducted to demonstrate the effectiveness of optimization, respectively. The results show that the machining process was improved obviously.

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