摘要
具有量子行为的粒子群优化(Quantum-behaved Particle Swarm Optim ization,QPSO)算法是继粒子群优化算法(Particle Swarm Optim ization,PSO)后,最新提出的一种新型、高效的进化算法。提出了运用QPSO算法设计的非线性观测器方法。该方法属于滚动时域估计方法,利用具有量子行为的粒子群算法优化获得系统状态的最优估计。仿真结果显示该方法对初始条件不敏感,具有很强的跟踪能力。
Quantum-behaved Particle Swarm Optimization（ QPSO）, is a new type, efficient swarm intelligence algorithm that proposed lately succeed to Particle Swarm Optimization （PSO）. A QPSO-based nonlinear observer design method was proposed. It belonged to moving horizon estimation method. Quantum-behaved particle swarm optimization algorithm was employed to find optimal estimation of the system states in this method. Simulation result showed that the proposed observer is not sensitive to the initial conditions and has a good tracking ability to the variations of the states.
出处
《计算机应用研究》
CSCD
北大核心
2007年第4期246-248,共3页
Application Research of Computers
基金
国家自然科学基金资助项目(60474030)
