摘要
为了平衡粒子群优化算法的全局和局部搜索能力,提出了一种多自适应策略粒子群优化算法。该算法在粒子进化过程中,采用了基于粒子进化度和局部开启混沌搜索相结合的速度自适应调节策略。将算法应用于模拟电路故障诊断的BP神经网络训练中,有效地解决了常规BP算法收敛速度慢、易陷入局部极小的问题。仿真结果表明算法具有较快的收敛速度和较高的诊断精度。
In order to balance local and global search ability of particle swarm optimization algorithm, a particle swarm optimization algorithm with multi-adaptive strategies （MAS-PSO） has been proposed. In the process of particle evolution, the algorithm adopted adaptive velocity setting strategies which were based on the evolution degree of particles and local opening chaotic search. The MAS-PSO is applied to BP neural network training of analog circuit fault diagnosis, and it solved effectively the problems of slow network convergence rate in conventional BP algorithm and easily falling into partial minimum. The simulation results show it works with quicker convergence rate and higher forecast precision.
出处
《计算机系统应用》
2012年第2期163-166,共4页
Computer Systems & Applications
基金
上海市科学技术委员会火炬计划(09HJC006100)
关键词
粒子群优化
神经网络
自适应策略
混沌搜索
故障诊断
particle swarm optimization
neural network
adaptive strategy
chaotic search
fault diagnosis
