自适应人工蜂群优化的混沌系统参数估计

为了对混沌系统未知参数进行准确估计,改进了人工蜂群优化算法,提出自适应人工蜂群算法的混沌系统参数估计方法。将混沌系统参数估计问题转化为多维变量数值优化问题,利用人工蜂群算法对未知参数进行导向随机搜索。在搜索过程中,通过种群优化程度和解的质量自适应地调整更新步长和解的尝试次数。以Lorenz混沌系统为例进行的仿真实验表明,该方法在无噪声和噪声强度较大的情况下均能够获得较好的估计结果,表现出较强的鲁棒性。

Adaptive artificial bee colony optimization for parameter estimation of chaotic systems

In order to accurately estimate the unknown parameters for chaotic systems, we improve the artificial bee colony optimization algorithm and propose an adaptive artificial bee colony optimization algorithm. The proposed method first formats the problem of parameter estimation for chaotic systems to a multidimensional variable optimization problem; then, use the artificial bee colony optimization algorithm to search the unknown parameters in a guided random manner. During the search process, the method will adaptively adjust the step size and the solution trial limits based on the optimum degree of the population and the quality of the solutions. The numerical simulation on the classic Lorenz chaotic system demonstrates that the proposed method is robust and can obtain accurate estimation for chaotic systems without noise or with intensive noise.