摘要
将人工蜂群算法用于非线性系统模型的参数估计,通过对谷氨酸菌体生长模型的参数估计进行验证,并与人工神经网络、遗传算法和微粒群算法的优化结果进行了比较.仿真试验结果表明:人工蜂群算法对非线性系统模型的参数估计精度高于人工神经网络、遗传算法和微粒群算法的参数估计精度,为非线性系统模型参数估计提供了一种有效的途径.
In this paper, the artificial bee colony algorithm is used to estimate the parameters of nonlinear system model. The effectiveness o f the artificial bee colony algorithm is tested by parameter estimation of glutamic acid bacterium growth model. The optimum results of artifi- cial bee colony algorithm method was compared with artificial neural network method, genetic algorithm method and particle swarm optimization method respectively. The experimental re- sults show that the artificial bee colony algorithm method estimation precisions of parameters of nonlinear system model is higher than that of artificial neural network method, genetic algo- rithm method and particle swarm optimization method, provides an effective method to estimate parameters of nonlinear system model.
出处
《数学的实践与认识》
CSCD
北大核心
2013年第16期142-147,共6页
Mathematics in Practice and Theory
基金
南京师范大学泰州学院资助项目(Q201232)
关键词
人工蜂群算法
非线性系统
参数估计
artificial bee colony algorithm
nonlinear system
parameter estimation
