欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
模糊球壳聚类算法（FCSS）广泛应用于模式识别与机器学习领域。由于其采用传统的基于梯度法和交替寻优策略求解模型,对初始值比较敏感,往往只能得到模型的局部极值点,从而影响聚类的效果,甚至使所采用的方法失效。本文将现代全局优化方法之一的粒子群优化算法与模糊球壳聚类算法（FCSS）相结合,利用粒子群算法良好的全局收敛能力来改善传统聚类算法易于陷入局部极值的缺陷,从而得到一种新的球壳聚类算法（PSO-FCSS）,数值实验表明,新方法对球壳形数据有令人满意的聚类效果。
英文摘要：
Fuzzy C-spherical shell cluster algorithm（FCSS） is widely applied to pattern recognition and machine learning.The classical clustering algorithms are based on gradient method and alternative optimization strategy;its disadvantages are sensitive to the initial values and easy to trap into a local optimal solution,affecting the clustering effect,even causing a false result.Using pso＇s better ability of finding the global optimum,a new spherical shell clustering algorithm called PSO-FCSS,which is proposed through integrating the FCSS algorithm with the particle swarm optimization（PSO）.As shown by the results of the computational tests,the clustering quality is satisfactory.
同期刊论文项目
同项目期刊论文
Development of an advanced X-ray detector for inspecting inner microscopic structural details in ind
Non-uniformity emendation technique for amorphous silicon flat-panel detectors used for industrial X
期刊信息
位置：