| 基于神经网络模型求取注记配置最优解 |
| |
| 引用本文： | 樊红,张祖勋,杜道生,张剑清.基于神经网络模型求取注记配置最优解[J].武汉大学学报(信息科学版),1998(1). |
| |
| 作者姓名： | 樊红 张祖勋 杜道生 张剑清 |
| |
| 作者单位： | 武汉测绘科技大学测绘遥感信息工程国家重点实验室 |
| |
| 摘 要： | 提出了一种点状注记自动配置的实用方法。其核心算法采用基于Ｈｏｐｆｉｅｌｄ神经网络模型求取点要素注记配置的最优解，克服了传统的冲突－回溯方法的不足。实验证明，该方法具有较好的性能和效率。
|
| 关 键 词： | Hopfield神经网络 能量函数 注记配置 |
A Hopfield Nerual Network Algorithm of Finding the Best Position for Point Annotation of Map |
| |
| Fan Hong,Zhang Zuxun,Du Daosheng,Zhang Jianqing.A Hopfield Nerual Network Algorithm of Finding the Best Position for Point Annotation of Map[J].Geomatics and Information Science of Wuhan University,1998(1). |
| |
| Authors: | Fan Hong Zhang Zuxun Du Daosheng Zhang Jianqing |
| |
| Abstract: | This paper presents a method of adding annotation to the map especially for the point feature.This method overcomes the shortcoming of traditional methods e.g.Conflict Tracebacking method.It's kernel algorithm that use the Hopfileld neural network to find the best position of feature annotation.The experimental results of running in HP workstation prove that this algorithm has a fairy permanence and high speed. |
| |
| Keywords: | hopfield nerual network energy function annotation placement |
| 本文献已被 CNKI 等数据库收录！ |
