部分精英策略并行遗传优化的神经网络盲均衡

王尔馥; 郑远硕; 陈新武

doi:10.11959/j.issn.1000-436x.2016148

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部分精英策略并行遗传优化的神经网络盲均衡

学术通信 | 更新时间：2024-06-05

- 部分精英策略并行遗传优化的神经网络盲均衡
- Neural network blind equalization optimized by parallel genetic algorithm with partial elitist strategy
- 通信学报 2016年37卷第7期页码：193-200
- 作者机构：
  
  黑龙江大学电子工程学院，黑龙江哈尔滨 150080
- 作者简介：
  
  [ "王尔馥（1980-），女，黑龙江哈尔滨人，博士，黑龙江大学副教授、硕士生导师，主要研究方向为盲信号分离及盲均衡。" ]
  [ "郑远硕（1994-），女，海南乐东人，黑龙江大学硕士生，主要研究方向为通信信号盲提取及数字化技术。" ]
  [ "陈新武（1992-），男，福建仙游人，黑龙江大学硕士生，主要研究方向为盲均衡及其硬件实现。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61571181);国家自然科学基金资助项目(61302074)
- DOI：10.11959/j.issn.1000-436x.2016148
  中图分类号： TN911.5
- 网络首发：2016-07，
  
  纸质出版：2016-07-25
- 稿件说明：
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王尔馥, 郑远硕, 陈新武. 部分精英策略并行遗传优化的神经网络盲均衡[J]. 通信学报, 2016,37(7):193-200.

Er-fu WANG, Yuan-shuo ZHENG, Xin-wu CHEN. Neural network blind equalization optimized by parallel genetic algorithm with partial elitist strategy[J]. Journal of Communications, 2016, 37(7): 193-200.
王尔馥, 郑远硕, 陈新武. 部分精英策略并行遗传优化的神经网络盲均衡[J]. 通信学报, 2016,37(7):193-200. DOI： 10.11959/j.issn.1000-436x.2016148.

Er-fu WANG, Yuan-shuo ZHENG, Xin-wu CHEN. Neural network blind equalization optimized by parallel genetic algorithm with partial elitist strategy[J]. Journal of Communications, 2016, 37(7): 193-200. DOI： 10.11959/j.issn.1000-436x.2016148.

摘要

针对高维非凸代价函数下神经网络盲均衡算法收敛速度慢、容易陷入局部极小值的缺点，提出了一种组群并行遗传优化神经网络的方法。根据神经网络拓扑结构进行个体编码，设置控制码和权重系数码以实现对网络拓扑结构和网络权重同时优化。优化迭代过程中根据适应度对个体排序分组，以融合不同遗传算子条件下遗传算法的优势。部分精英策略有效避免最优个体把持进化过程引发早熟的现象。非线性信道条件下的仿真结果证明方法具有更好的收敛性能。

Abstract

Owing to the disadvantage of slow convergence and easy to fall into local minimum of the neural network blind equalization algorithm under high dimensional and non-convex cost function

a parallel genetic algorithm (GA) with partial elitist strategy was proposed to optimize neural network training. According to the neural network topology

individual coding

the control code and the weights were set up to realize the network topology structure and the network weights simultaneously. The individual group was sorted according to the adaptation degree of the optimization iterative process

in order to integrate the advantages of genetic algorithm under the conditions of different genetic operators. Some elite strategies effectively avoid the phenomenon of premature phenomena caused by the optimal individual control in the process of evolution. The simulation results under the nonlinear channel condition show that the method has better convergence performance.

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references

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