Refrigerated Showcase Fault Analysis by a Correntropy Based Artificial Neural Network Using Particle Swarm Optimization

被引：0

作者：

Otaka, Naoya ^{[1
]}

Fukuyama, Yoshikazu ^{[1
]}

Kawamura, Yu ^{[2
]}

Murakami, Kenya ^{[2
]}

Santana, Adamo ^{[2
]}

Iizaka, Tatsuya ^{[2
]}

Matsui, Tetsuro ^{[2
]}

机构：

[1] Meiji Univ, Sch Interdisciplinary Math Sci, Tokyo, Japan

[2] Fuji Elect Co Ltd, Tokyo, Japan

来源：

2018 57TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) | 2018年

关键词：

Fault analysis; refrigerated showcase; neural network; correntropy; particle swarm optimization;

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：

This paper proposes refrigerated showcase fault analysis by a correntropy based Artificial Neural Network (ANN) using Particle Swarm Optimization (PSO). Effectiveness of the proposed method is verified by comparison with conventional weighted least square (WLS) based ANNs using back-propagation (BP) and PSO with actual showcase data.

引用

页码：1508 / 1513

页数：6

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