Pattern Classification Based on Self-organizing Feature Mapping Neural Network

被引：3
|
作者：
Ding Shuo [1 ]
Chang Xiao-heng [1 ]
Wu Qing-hui [1 ]
机构：
[1] Bohai Univ, Coll Engn, Jinzhou 121013, Liaoning, Peoples R China
来源：
RENEWABLE ENERGY AND ENVIRONMENTAL TECHNOLOGY, PTS 1-6 | 2014年 / 448-453卷
关键词：
Self-organizing feature mapping (SOFM); Artificial neural networks (ANN); Pattern classification; Simulation; MATLAB;
D O I：
10.4028/www.scientific.net/AMM.448-453.3645
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Traditional pattern classification methods are not always efficient because sample data sets are sometimes incomplete and there are exceptions and counter examples. In this paper, SOFM neural network is applied in pattern classification of two-dimensional vectors after analysis of its structure and algorithm. The method to establish SOFM network via MATLAB7.0 is introduced before the network is applied to classify two-dimensional vectors. The adjustment process of weight vectors together with classification performance of SOFM model are also tested in the condition of different number of training steps. The simulation results show that the classification approach based on SOFM model is effective because of its fast speed, high accuracy and strong generalization ability.
引用
收藏
页码：3645 / 3649
页数：5
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