Parameters Selection Method of Multiple Vortex-Ring Microburst Model Based on Nested Particle Swarm Optimization

WU Yang; JIANG Shou-da

doi:10.3969/j.issn.0372-2112.2012.01.034

您当前的位置：

首页 >

文章列表页 >

Parameters Selection Method of Multiple Vortex-Ring Microburst Model Based on Nested Particle Swarm Optimization

更新时间：2025-07-16

- Parameters Selection Method of Multiple Vortex-Ring Microburst Model Based on Nested Particle Swarm Optimization
- Acta Electronica Sinica Vol. 40, Issue 1, Pages: 204-208(2012)
- 作者机构：
  
  哈尔滨工业大学电气工程及自动化学院,黑龙江,哈尔滨,150080
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2012.01.034
  CLC： TP202.7
- Published：2012
- 稿件说明：
移动端阅览
WU Yang, JIANG Shou-da. Parameters Selection Method of Multiple Vortex-Ring Microburst Model Based on Nested Particle Swarm Optimization[J]. Acta Electronica Sinica, 2012, 40(1): 204-208.
DOI：

WU Yang, JIANG Shou-da. Parameters Selection Method of Multiple Vortex-Ring Microburst Model Based on Nested Particle Swarm Optimization[J]. Acta Electronica Sinica, 2012, 40(1): 204-208. DOI： 10.3969/j.issn.0372-2112.2012.01.034.

摘要

提出一种使用嵌套粒子群算法

根据微下击暴流水平垂直风速最大峰值比选择多涡环微下击暴流模型参数的方法.设计了包括目标群和中间群的嵌套式粒子群结构

使用中间群寻找水平垂直最大风速

并使用目标群最终选择模型参数.用该方法计算了水平垂直风速峰值比在0.3~0.7之间的多组多涡环微下击暴流模型参数

最终生成的微下击暴流场的水平垂直风速峰值比与预设值误差的数量级在10

-4

以内.

Abstract

A parameter selecting method of Multiple Vortex Ring Microburst Model(MVRMM) based on NPSO (Nested Particle Swarm Optimization) is propesed

which can select the parameters of MVRMM by any proportion between maximal horizontal velocity and maximal vertical velocity.The nested particle swarm structure is designed which contained parent swarm and subswarms.Find the maximal horizontal and vertical velocity with subswarms

and find the optimal parameters with parent swarm.Calculate several parameters of MVRMM by this method

which the proportion between maximal horizontal velocity and maximal vertical velocity is between 0.3 to 0.7.And the error between the factual proportion and the setting proportion is within 10

-4

关键词

Keywords

references

Views

1587

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Particle Swarm Optimization Algorithm for FIR Digital Filters Design

A Stochastic Maximum Likelihood Algorithm Based on Improved PSO

Improved Particle Swarm Optimization Algorithm Based on Opposite Learning of Refraction

Test Case Generation Based on Orthogonal Exploration and Particle Swarm Optimization

Pattern Synthesis of Array Antenna with Modified Quantum Particle Swarm Optimization Algorithm

Related Author

XU Qi

ZHAO Min

ZHANG An

LI Hui

ZHANG He

CHEN Hai-hua

LIU Fen

SONG Hua-jun

Related Institution

Department of Electronic Engineering,Northwestern Polytechnical University

College of Computer and Communication Engineering China University of Petroleum Shandong Qingdao China

College of Information and Control Engineering China University of Petroleum(East China) Shandong Qingdao 2665802 China

College of Computer and Communication Engineering, China University of Petroleum

College of Information and Control Engineering, China University of Petroleum(East China), Shandong, Qingdao 2665802

⁰