基于改进人工蜂群算法的非线性系统参数辨识  

Nonlinear System Parameter Identification Based on Improved Artificial Bee Colony Algorithm

作　　者：肖晓 王明春[1] 张雨飞[1] 吴君 

机构地区：[1]东南大学能源与环境学院能源信息自动化系

出　　处：《工业控制计算机》2019年第11期65-67,共3页Industrial Control Computer

摘　　要：针对人工蜂群算法存在的收敛速度慢,易陷入局部最优等问题,提出一种结合遗传算法的交叉算子,并在算法后期引入高斯变异和混沌扰动的改进人工蜂群算法。为验证改进算法的性能和有效性,用典型测试函数进行对比测试,并将改进算法应用于非线性传递函数模型的参数辨识中。实验结果表明,改进的算法收敛速度快,收敛精度高,辨识效果好。An improved artificial bee colony algorithm,which is combined with the crossover operator of genetic algorithm,and added in Gauss mutation and chaotic perturbation in the later stage of the algorithm,is proposed in this paper to overcome the drawbacks of the standard ABC Algorithm such as slow convergence speed and easily trapping into local optimum.To verify the performance and effectiveness of the improved algorithm,several typical test functions are tested and compared,and the parameter of the nonlinear transfer function model is identified.

关 键 词：人工蜂群算法 交叉算子 高斯变异 混沌扰动 参数辨识 

分 类 号：S89[农业科学—特种经济动物饲养]