基于凸组合滤波的非线性主动噪声控制算法

Nonlinear Active Noise Control Algorithm Based on Convex Combined Filtering

滤波S最小均方误差算法(FSLMS)可用于解决主动降噪中的非线性问题。MCNFSLMS算法是将两个FSLMS滤波结构通过联合参数进行组合,从而实现快速收敛和低稳态误差。然而该算法缺乏交叉项,且联合参数在迭代过程中需要进行符号判断,影响了算法性能。为此,提出一种基于FSLMS与广义函数链接人工神经网络(GFLANN)的凸组合算法,其通过GFLANN结构引入交叉项,并用一个新函数作为联合参数,避免了符号判断。在3个不同的非线性条件下,验证了该组合结构比单一结构具有更好的性能,并且与组合结构MCNFSLMS相比,该算法在降噪性能上分别提升了2dB、1dB、4dB,在减少了计算量的同时,具有更低的稳态误差。实验结果表明,该算法能更好地解决主动降噪过程中的非线性问题。

The filtered-S least mean square(FSLMS)algorithm can be used to solve the nonlinear problem in active noise reduction.The MCNFSLMS algorithm combines two FSLMS filtering structures through joint parameters,achieving fast convergence and low steady-state error.However,the performance of the algorithm is affected by the lack of cross terms and the symbol judgment of the joint parameters in the iteration process.Therefore,a convex combined algorithm based on FSLMS and generalized functional link artificial neural network(GFLANN)is proposed.The cross term is introduced through GFLANN structure,and a new function is used as the joint parameter to avoid the judgment of symbol.Under three different nonlinear conditions,it is verified that the proposed combined structure has better performance than a single structure,and compared with the combined structure MCNFSLMS,the algorithm has improved noise reduction performance by 2dB,1dB,and 4dB respectively,at the mean time,reducing the amount of calculation,and having a lower steady-state error.The experimental results verified that this algorithm can better solve the nonlinear problem in active noise reduction.