摘要： 在传统小波神经网络盲均衡算法的基础上,提出一种基于动态参数调整的自适应步长盲均衡算法。根据均衡器输出信号的大小,并结合输出信号功率与收敛性质的关系,对迭代步长因子进行改进,实现迭代步长因子的动态调整。通过多组对比实验对可调参数进行优化选取,从而克服收敛速度与收敛精度相互制衡的问题。实验结果表明,该算法的性能指标与预期结果基本相符,尤其在迭代次数较多时,相比传统小波神经网络盲均衡算法,具有更快的收敛速度与更高的收敛精度。

关键词: 小波变换, 神经网络, 盲均衡算法, 可调参数, 迭代步长

Abstract: On the basis of the traditional wavelet neural network blind equalization algorithm,this paper proposes a blind equalization algorithm of adaptive step size based on dynamic parameter adjustment.According to the equalizer of output signal size,it combines with the relationship between output signal power and convergence properties,and realizes dynamic adjustment of iterative step size factor.It optimizes and selects tunable parameters through several comparative experiments,and overcomes the mutual restriction problem of convergence rate and convergence precision.Experimental results show that performance indicator of this algorithm is basically consistent of the expected results.Especially in the case of more iterations,compared with traditional wavelet neural network blind equalization algorithm,it has faster convergence rate and higher convergence accuracy.

Key words: wavelet transform, neural network, blind equalization algorithm, tunable parameter, iteration step size

中图分类号: 

• TN911.5