基于FPSO-SA算法的威布尔分布参数估计研究

为解决威布尔分布等复杂分布模型采用常规方法很难直接进行参数估计的问题, 提出了基于模糊粒子群模拟退火算法的威布尔分布参数估计。该算法根据粒子个体纵向和横向运动特性, 引入模糊逻辑推理动态调整惯性权值因子, 提高了粒子群算法(PSO: Particle Swarm Optimization)的收敛速率； 将上述模糊粒子群算法(FPSO: Fuzzy Particle Swarm Pptimization)与模拟退火算法(SA: Simulated Annealing)结合, 以FPSO算法的速度位置更新公式作为SA算法的状态生成函数, 再运用Metropolis算法以概率接受新状态, 获得全局最优参数估计值。将基于上述智能算法的参数估计法运用到威布尔分布参数估计中, 提高了参数估计精度。实际应用表明, 该参数估计方法在复杂分布模型参数估计中具有可行性和有效性。

Research on Estimating Parameters of Weibull Distribution Model Based on FPSO-SA

To solve the problem of the difficulty in estimating the parameters of Weibull distribution model,a new algorithm based on FPSO（Fuzzy Particle Swarm Optimization） with SA（Simulated Annealing） for estimating the parameters of Weibull distribution model was presented.In this intelligent algorithm,the inertia factor of PSO was turned by adopting fuzzy logic reasoning to improve the convergence rate of the PSO（Particle Swarm Optimization） according to the characteristic of particle＇s motion trajectory in longitudinal direction and lateral direction.Then combine the FPSO and SA,use the update formulas of FPSO as the state generating function of SA,and use Metropolis algorithm to accept the new state with probability to get the global optimal parameters＇ estimations.The parameter estimation method based on the intelligent algorithm above-mentioned was used to estimate the parameters of Weibull distribution model,and the parameter estimation accuracy was improved.Practical application in the complex distribution model parameter estimation showed that the method was feasible and effective.