摘要
本研究提出了一种基于改进遗传算法辨识Volterra级数模型的方法。该方法根据Volterra核与系统输出的相关程度来调整模型结构,利用重启策略与自适应搜索范围解决进化停滞与算法早熟收敛等问题。通过仿真试验将改进遗传算法与标准遗传算法、量子粒子群算法进行比较。结果表明,该方法在辨识精度、收敛速度及抗噪性能等方面明显优于其他方法。
In this paper,a method for identifying Volterra series model based on improved genetic algorithm(IGA)is proposed.This method adjusts the model structure according to the correlation between Volterra kernel and system output,uses the restart strategy and adaptive search range to solve the problems of evolutionary stagnation and premature convergence of the algorithm.Through simulation experiments,the IGA method is compared with standard genetic algorithm(GA)and quantum particle swarm optimization(QPSO)algorithm.The analysis results indicate that the IGA method is superior to other methods in identification accuracy,convergence speed and anti-noise performance.
作者
王鑫超
张宾
WANG Xinchao;ZHANG Bin(School of Mechanical Engineering,North China University of Water Resources and Electric Power,Zhengzhou 450045,China)
出处
《河南科技》
2022年第5期24-27,共4页
Henan Science and Technology
