摘要
针对传统分数阶最小均方算法收敛性能较差的问题,提出了一种改进型分数阶最小均方算法。首先,利用分数阶微积分和多新息理论,从新息修正的角度提出了一种基于辅助模型多新息分数阶的最小均方算法(auxiliary model least mean square identification algorithm with multi-innovation and fractional order,AM-MFLMSI)。该算法在每次迭代时既使用当前数据,又使用了历史的数据,提高了收敛速度,同时还改善了参数估计精度。其次,分析了AM-MFLMSI的收敛性。然后,通过选取不同的分数阶和新息长度,比较分析了两者对算法性能的影响。最后,通过仿真实例,将AM-MFLMSI与其他分数阶算法作比较,进一步验证了所提算法的有效性。
An improved fractional order least mean square identification was presented to solve the poor convergence performance of traditional fractional least mean square algorithm.Firstly,using fractional calculus and multi-innovation theory,a based auxiliary model least mean square identification algorithm with multi-innovation and fractional order(AM-MFLMSI)was presented from the perspective of innovation modification.Both the current data and the historical data at each iteration were used in the proposed algorithm,the convergence velocity and precision was improved by the proposed algorithm.After that,the convergence of AM-MFLMSI was analyzed.Then,by taking different fractional order and innovation length,the influence of them on the performance of the algorithm was analyzed.Finally,compared AM-MFLMSI with other fractional order algorithms,the effectiveness of the proposed algorithm was verified by a simulation example.
作者
查琴
王宏伟
ZHA Qin;WANG Hong-wei(School of Electrical Engineering, Xinjiang University, Urumqi 830047, China;School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China)
出处
《科学技术与工程》
北大核心
2021年第32期13765-13773,共9页
Science Technology and Engineering
基金
国家自然科学基金(61863034)。
关键词
辅助模型
多新息
分数阶
算法收敛性分析
auxiliary models
multi-innovation
fractional order
algorithm convergence analysis