Fast convergence FLANN algorithm based on an adaptive mixing structure

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (10) : 180-186.

LI Huanhuan

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Abstract

Among the nonlinear active noise control methods, the functional link artificial neural network (FLANN) algorithm is one of the most commonly used algorithms.The FLANN has a better noise reduction result, but its convergence speed is slow.In order to solve this problem, a new combined bilinear FLANN (CBFLANN) algorithm by combining the bilinear filtered-x LMS (BFXLMS) algorithm with the FLANN algorithm using an adaptive mixing parameter was proposed.Without decreasing the noise reduction capability of FLANN, the convergence speed of FLANN was improved, such that the fast convergence and low steady-state error for the FLANN were achieved at the same time.To verify the performance of the proposed CBFLANN algorithm, several simulation experiments were conducted.The results show that the CBFLANN has both the convergence rate of BFXLMS and the steady-state error of FLANN at the same time.The proposed algorithm can provide a solution to the difficulty in considering both the convergence rate and the steady-state error for the conventional active noise control algorithms.It has a strong practical application value.

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nonlinear active noise control / functional link artificial neural network (FLANN) / convergence rate / steady-state error

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LI Huanhuan. Fast convergence FLANN algorithm based on an adaptive mixing structure[J]. Journal of Vibration and Shock, 2021, 40(10): 180-186

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