粒子群优化算法拟合Logistic方程的研究

Study on Fitting Logistic Equations with Particle Swarm Optimization Algorithms

提出了一种新的拟合Logistic方程与估计其参数的方法.首先利用三段和法对参数进行初步估计,得到参数的粗略值,然后利用粒子群优化算法进行寻找最优参数,并对所得结果进行统计检验.结果表明三段和法与粒子群优化算法相结合具有很强的拟合Logistic方程的能力.此方法可推广应用到对其他非线性生长曲线方程的拟合.

A new method was developed for fitting of Logistic equations and estimating the parameters.Rough estimation of the parameters was conducted first with three-sum methods,and then the optimum parameters were found by particle swarm optimization algorithm（PSO）.Statistical test was applied to the data.The results showed that the combined method of three-sum method and particle swarm optimization algorithm can fit the Logistic equations well.This method can be generalized to fit other nonlinear growth curve equations.