搜索结果: 1-5 共查到“particle swarm optimization”相关记录5条 . 查询时间(0.078 秒)
MULTI-PATCHES IRIS BASED PERSON AUTHENTICATION SYSTEM USING PARTICLE SWARM OPTIMIZATION AND FUZZY C-MEANS CLUSTERING
Particle swarm optimization Fuzzy c-means Taylor’s series expansion weighted mean Hamming distance Iris recognition system
2017/6/19
Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented ...
Design of Non-Uniform Linear Array via Linear Programming and Particle Swarm Optimization and Studies on Phased Array Calibration
linear array linear programming particle swarm optimization side lobe level phased array calibration
2014/12/8
For a linear array, the excitation coefficients of each element and its geometry play an important role, because they will determine the radiation pattern of the given array. Side Lobe Level (SLL) is ...
COMPARISON OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM IN RATIONAL FUNCTION MODEL OPTIMIZATION
Rational Function Model (RFM) Particle Swarm Optimization (PSO) Genetic Algorithm (GA) Mathematical Modelling High Resolution Satellite Images (HRSIs)
2014/4/25
Rational Function Models (RFM) are one of the most considerable approaches for spatial information extraction from satellite images especially where there is no access to the sensor parameters. As the...
Asynchronous particle swarm optimization-based search with a multi-robot system: simulation and implementation on a real robotic system
Asynchronous particle optimization-based search a multi-robot system a real robotic system
2010/10/12
In this article we consider a version of the Particle Swarm Optimization (PSO) algorithm which is appropriate for search tasks of multi-agent systems consisting of small robots with limited sensing ca...
基于PSO与K-均值算法的农业超绿图像分割方法（Agriculture Extra-green Image Segmentation Based on Particle Swarm Optimization and K-means Clustering）
图像分割 微粒群算法 K均值算法
2009/9/11
为了解决K-均值算法对农业图像中常用的超绿特征2G—R—B图像分割效果不佳的缺点，提出一种基于微粒群与K均值算法的图像分割方法。先用K均值算法对图像进行快速分类，然后将分类结果作为其中一个微粒的结果，利用微粒群算法计算，最后用K-均值算法在新的分类基础上计算新的聚类中心，更新当前的位置，以得到最优的图像分割阈值。试验结果表明，改进算法对超绿特征2G—R—B图像能够准确分割目标，且对不同类型的农业超...