Convergence Analysis of a New MaxMin-SOMO Algorithm

被引：0
|
作者：
Khan, Atlas [1 ,3 ]
Qu, Yan-Peng [2 ]
Li, Zheng-Xue [1 ]
机构：
[1] Dalian Univ Technol, Dept Appl Math, Dalian 116024, Peoples R China
[2] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116026, Peoples R China
[3] Univ Sao Paulo, Dept Comp & Math FFCLRP, Ribeirao Preto, Brazil
来源：
INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING | 2019年 / 16卷 / 04期
基金：
中国博士后科学基金; 中国国家自然科学基金; 巴西圣保罗研究基金会;
关键词：
Optimization; self organizing map (SOM); SOM-based optimization (SOMO) algorithm; particle swarm optimization (PSO); genetic algorithms (GAs); SELF-ORGANIZING MAP;
D O I：
10.1007/s11633-016-0996-0
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The convergence analysis of MaxMin-SOMO algorithm is presented. The SOM-based optimization (SOMO) is an optimization algorithm based on the self-organizing map (SOM) in order to find a winner in the network. Generally, through a competitive learning process, the SOMO algorithm searches for the minimum of an objective function. The MaxMin-SOMO algorithm is the generalization of SOMO with two winners for simultaneously finding two winning neurons i.e., first winner stands for minimum and second one for maximum of the objective function. In this paper, the convergence analysis of the MaxMin-SOMO is presented. More specifically, we prove that the distance between neurons decreases at each iteration and finally converge to zero. The work is verified with the experimental results.
引用
收藏
页码：534 / 542
页数：9
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