Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring

被引：34

作者：

Oliveira, M. V. ^{[1
]}

Schirru, R. ^{[2
]}

机构：

[1] Inst Engn Nucl, CNEN, Div Instrumentacao & Confiabilidade Humana, BR-21945970 Rio De Janeiro, Brazil

[2] Univ Fed Rio de Janeiro, Nucl Engn Program, Lab Monitoracao Proc, BR-21945970 Rio de Janeiro, Brazil

来源：

PROGRESS IN NUCLEAR ENERGY | 2009年 / 51卷 / 01期

关键词：

Signal validation; Particle swarm optimization; Neuro-fuzzy system;

D O I：

10.1016/j.pnucene.2008.03.007

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

A neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed for monitoring the relevant sensor in a nuclear power plant (NPP) using the information of other sensors. The antecedent parameters of the ANFIS that estimates the relevant sensor signal are optimized by a PSO algorithm and consequent parameters use a least-squares algorithm. The proposed methodology to monitor sensor Output signals was demonstrated through the estimation of the nuclear power value in a pressurized water reactor using as input to the ANFIS six other correlated signals. The obtained results are compared to two similar ANFIS using one gradient descendent (GD) and other genetic algorithm (GA), as antecedent parameters' training algorithm. (c) 2009 Elsevier Ltd. All rights reserved.

引用

页码：177 / 183

页数：7

共 12 条

[1]

[Anonymous], 1994, Neural networks: a comprehensive foundation

[2] Adaptive fuzzy system for degradation study in nuclear power plants' passive components [J].