摘要
在Kohonen提出的SOM(self-organization map)神经网络的基础上,通过拓广SOM网络的获胜节点数量,引入惩罚修正因子,改进邻域和连接权函数等方法提出一种新的SOM即SOMDW(SOM with double-winner)模型。为了验证该模型的有效性,以旅行商问题(traveling salesman problem,TSP)为例对该模型进行检验,得到了满意的结果。另外为了增强SOMDW网络的动态聚类性能,提高解的精确性,还采用禁忌搜索的搜索方法。
Based on SOM （self-organization map） neural network developed by Kononen, a novel SOM model, namely SOMDW （SOM with double-winner） is proposed by increasing winner nodes and improving some functions such as neighbor function and connection weight function and introducing direction modifying factor in SOM network. In order to validate SOMDW model, an example of TSP （traveling salesman problem） is applied and some satisfying results are also obtained. In addition, in order to enhance the dynamic competition and clustering capability of SOMDW so that some accurate results is Obtained by using SOM, tabu-search method is also applied.
出处
《计算机工程与设计》
CSCD
北大核心
2007年第11期2637-2639,共3页
Computer Engineering and Design
基金
国家自然科学基金项目(60373099)
吉林省社会科学基金项目(2007234)
长春税务学院院级基金项目(2006016)。
关键词
SOM神经网络
双获胜节点
邻域函数
连接权函数
聚类
TSP问题
SOM neural network
double-winner winner
neighborhood function
connection weight function
clustering
TSP problem
