计算机工程

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基于动态参数调整的小波神经网络盲均衡算法

赵慧青,万智萍   

  1. (中山大学新华学院 信息科学系,广州 510520)
  • 收稿日期:2015-06-10 出版日期:2016-06-15 发布日期:2016-06-15
  • 作者简介:赵慧青(1978-),女,讲师、硕士,主研方向为网络通信、多媒体信息处理;万智萍,讲师、硕士。
  • 基金项目:
    广东省教育厅青年创新人才(自然科学类)基金资助项目(2014KQNCX253)。

Wavelet Neural Network Blind Equalization Algorithm Based on Dynamic Parameter Adjustment

ZHAO Huiqing,WAN Zhiping   

  1. (Department of Information Science,Xinhua College of Sun Yat-sen University,Guangzhou 510520,China)
  • Received:2015-06-10 Online:2016-06-15 Published:2016-06-15

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

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