摘要
在检测数据库重复记录的研究中,基于BP神经网络的检测(Duplicate Record Detection based on BP Neural Network,简称DRDBPNN)算法的性能与初始的参数设置有很大的关系,从而造成其性能不稳定的缺陷,因此本文提出了一种基于QPSO与BP神经网络的重复记录检测算法(Duplicate Record Detection based on Quantum Particle Swarm Optimization and BP Neural Network,简称DRDQPSQBPNN)。仿真表明,该算法能够有效地提升重复记录的检测效率。
in the test database Duplicate records in the study,based on the BP Neural Network Detection(Duplicate Record Detection-based on the BP Neural Network,referred to as DRDBPNN)performance of the algorithm and the initial parameters set has a lot to do,resulting in the defect of its performance is not stable,so this paper proposes a Duplicate records Detection based on QPSO with BP Neural Network algorithm(Duplicate Record Detection-based on Quantum Particle Swarm Optimization and BP Neural Network,hereinafter referred to as DRDQPSQBPNN).The simulation shows that the algorithm can effectively improve detection efficiency of duplicate records.
出处
《网络安全技术与应用》
2014年第11期67-68,共2页
Network Security Technology & Application
关键词
量子粒子群算法
神经网络
重复记录
数据库
quantum particle swarm optimization(pso)
Neural network
Duplicate records
The database