Finding the Shortest Path Based on Delay PCNN

GU Xiao-dong; YU Dao-heng; ZHANG Li-ming

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Finding the Shortest Path Based on Delay PCNN

更新时间：2025-07-16

- Finding the Shortest Path Based on Delay PCNN
- Acta Electronica Sinica Vol. 32, Issue 9, Pages: 1441-1443(2004)
- 作者机构：
  
  1. 复旦大学电子工程系,上海,200433
  2. 北京大学电子学系视觉与听觉信息处理国家重点实验室,北京,100871
  3. 复旦大学电子工程系上海,200433
  4. 北京大学电子学系视觉与听觉信息处理国家重点实验室北京,100871
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183
- Published Online：25 September 2004，
  
  Published：2004
- 稿件说明：
移动端阅览
GU Xiao-dong, YU Dao-heng, ZHANG Li-ming. Finding the Shortest Path Based on Delay PCNN[J]. Acta Electronica Sinica, 2004, 32(9): 1441-1443.
DOI：

GU Xiao-dong, YU Dao-heng, ZHANG Li-ming. Finding the Shortest Path Based on Delay PCNN[J]. Acta Electronica Sinica, 2004, 32(9): 1441-1443. DOI：

摘要

本文在脉冲耦合神经网络(PCNN-Pulse Coupled Neural Network)的基础上

提出了时延脉冲耦合神经网络(DPCNN-Delay PCNN)

并将其成功地用于求解最短路径

同时给出了基于DPCNN的最短路径求解算法.Caulfield与Kinser提出了用PCNN求解迷宫问题的方法

虽然他们的方法也可用于求解最短路径

但所需神经元的数量巨大

而本文的方法所需的神经元的数量远小于他们的方法.同时

本文的方法充分利用了DPCNN脉冲快速并行传播的特点

可迅速地求出最短路径

其所需的计算量仅正比于最短路径的长度

与路径图的复杂程度及路径图中的通路总数无关.计算机仿真结果表明

采用本文的方法

用少量的神经元就可迅速地求出最短路径.

Abstract

This paper presents DPCNN(Delay Pulse Coupled Neural Network) based on PCNN and uses DPCNN to find the shortest path successfully.Meanwhile

the algorithm of finding the shortest path based on DPCNN is described.Caulfield and Kinser introduced the PCNN method to solve the maze problem and although their method also can be used to find the shortest path

a large quantity of neurons are needed.However

the approach proposed in this paper needed very fewer neurons than proposed by Caulfield and Kinser.In the meantime

due to the pulse parallel transmission characteristic of DPCNN

the approach proposed can find the shortest path quickly.The computational complexity of our approach is only related to the length of the shortest path

and independent to the path graph complexity and the number of existing paths in the graph.The results of computer simulations show that by using the approach proposed in this paper

we can use a small quantity of neurons to find the shortest path quickly.

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