Passive optical networks optimization planning based  on artificial intelligence methods

CHEN Ya  hua  1; LIU Jin  gao  2; ZHAO Wen  wu  2; BIAN Yu  cun  2

doi:10.13756/j.gtxyj.2001.03.010

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Passive optical networks optimization planning based on artificial intelligence methods

- Passive optical networks optimization planning based on artificial intelligence methods
- Study on Optical communication Issue 3, Pages: 35-38(2001)
- 作者机构：
  
  1. 上海电信技术研究所!上海200122
  2. 华东师范大学电子科学技术系!上海200062
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2001.03.010
  CLC： TN929.11
- Published：2001，
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[1]陈亚华,刘锦高,赵文武,卞玉存.基于人工智能方法的无源光网络优化规划[J].光通信研究,2001(03):35-38.

CHEN Ya hua 1, LIU Jin gao 2, ZHAO Wen wu 2, et al. Passive optical networks optimization planning based on artificial intelligence methods. [J]. (3):35-38(2001)
[1]陈亚华,刘锦高,赵文武,卞玉存.基于人工智能方法的无源光网络优化规划[J].光通信研究,2001(03):35-38. DOI： 10.13756/j.gtxyj.2001.03.010.

CHEN Ya hua 1, LIU Jin gao 2, ZHAO Wen wu 2, et al. Passive optical networks optimization planning based on artificial intelligence methods. [J]. (3):35-38(2001) DOI： 10.13756/j.gtxyj.2001.03.010.

摘要

使用人工智能方法对 PON网络拓扑结构进行优化

以富士通 FPX- 1 0 0 0无源光网络系统为例进行了仿真实验

并对遗传算法、增添算法和 Hopfield网络方法进行比较

仿真结果表明遗传算法明显优于增添算法和 Hopfield网络方法 .遗传算法可使网络建设代价最小化

它非常好地解决了 PON规划问题

Abstract

This paper uses artificial intelligence methods to optimize the PON topology. The simulation experiments of PON systems such as FPX 1000 are carried out. The genetic algorithm （GA）

add algorithm （AA） and Hopfield net approach （HNA） are compared

The simulation results show that GA is better than AA and HNA. GA can minimize installation cost of PON and realize the optimization of the passive optical networks with satisfaction.

关键词

无源光网络遗传算法增添算法Hopfield网络方法网络规划

Keywords

passive optical networkgenetic algorithmadd algorithmHopfield net approachnetwork planning

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