Non-linear mappings based on particle swarm optimization

被引：0

作者：

Figueroa, CJ ^{[1
]}

Estévez, PA ^{[1
]}

Hernández, RE ^{[1
]}

机构：

[1] Univ Chile, Dept Elect Engn, Santiago, Chile

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5 | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Non-linear mapping methods that minimize the Sammon stress based on Particle Swarm Optimization (PSO) are proposed. The task considered is the mapping or the codebook vectors generated by the Neural Gas (NG) network onto a two-dimensional space. Three methods are explored: the direct application of the traditional PSO, the initialization of PSO with TOPNG, and a dynamically Growing PSO. These methods are compared with the Sammon's mapping and TOPNG in terms of the Sammon stress and the topology preservation measure q(m). The best results are obtained when PSO is initialized with TOPNG.

引用

页码：1487 / 1492

页数：6

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