Abstract:In this paper, an improved fractional order least mean square identification is 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) is presented from the perspective of innovation modification. The algorithm uses both the current data and the historical data at each iteration, which improves the convergence velocity and precision. After that, we analyze the convergence of AM-MFLMSI. Then, by taking different fractional order and innovation length, the influence of them on the performance of the algorithm is analyzed. Finally, compared AM-MFLMSI with other fractional order algorithms, the effectiveness of the proposed algorithm is verified by a simulation example.