基于人工蜂群算法的混沌系统参数估计

为了更准确的估计混沌系统的未知参数,提出了一种基于人工蜂群算法的混沌系统参数辨识方法,该方法将混沌系统中参数估计转化为多维变量的函数优化问题,利用搜索方程对多维空间变量进行充分搜索,通过优化人工蜂群算法计算估计值与真值之间的均方差,从而估计出混沌系统的参数. Lorenz混沌系统的参数辨识仿真实验结果表明了该方法的可行性,并且算法收敛速度快,估计精度高.

Artificial Bee Colony Algorithm in Parameter Estimation of Chaotic Systems

In order to estimate the unknown parameters of the chaotic systems more accurately, we propose a method for the parameter estimation of chaotic systems based on the artificial bee colony algorithm. This method converts the parameter estimation of chaotic systems to the function optimization problem of a multi-dimensional variable and then uses a search equation to fully search the multi-dimensional spatial variable. Furthermore, the optimized artificial bee colony algorithm is applied to calculate the mean square error between the estimated value and the true value, so as to realize the parameter estimation in the chaotic systems. In addition, the results of parameter estimation simulation of the Lorenz chaotic system indicate the feasibility of the proposed method. Besides, the improved algorithm has fast convergence and high estimation accuracy.