摘要
认识和掌握贝叶斯网络架构是数据求索和知识创新范畴的主要探讨方式之一,当处在网络购架寻觅范围较广的条件下,过去的二值粒子组合改良计算方法时常表现出聚拢速率低,很可能滑入局部范围取优、认识和掌握精准度较低的劣势.在以往二值粒子组合改进计算方法的前提下,依托互信息粒子组合计算方法的初期化过程,减小计算方法的寻觅范围,并且设置新型的演变模型取代以往的演变方程,从而使改良后的计算方法拥有较大的求优功能.选取ASIA网络系统当作模仿样板,再和以往计算方法相比,最终说明改进计算方法可以利用不多的重复换代过程寻觅到极优的方程解,而且总体上不会增添计算过程的繁琐程度.
Bayesian network structure learning is one of the important research techniques in the do‐main of data mining and know ledge discovery ,w hen the search space of the netw ork structure is bigger , traditional binary particle algorithms often have some defects such as low convergent speed ,falling easily into local optimum and low precision We improve the classic binary particle swarm optimization algo‐rithm in two respects：particle initialization and update process ;the improved algorithm has stronger op‐timization ability .We compare the proposed algorithm with the original algorithm using the ASIA net‐work .The results and their analysis show preliminarily that the proposed algorithm is able to find the better solution with less number of iterations ,without increasing the complexity basically .
出处
《聊城大学学报（自然科学版）》
2015年第1期83-87,共5页
Journal of Liaocheng University:Natural Science Edition
基金
安徽省委宣传部社科规划办项目(AHSKY2014D102)资助
关键词
贝叶斯网络
数据求索
粒子组改良
Bayesian networks, data mining, particles warm optimization （PSO）
