A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis

Zhide Liu; Jiabin Chen; Yongqiang Han; Chunlei Song

doi:10.1109/IEEM.2009.5373007

A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis

Zhide Liu^*
, Jiabin Chen
, Yongqiang Han
, Chunlei Song

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this paper, a new adaptive genetic algorithm (GA)-based radial basis function (RBF) neural network with optimal selection clustering algorithm (OSCA) is proposed for the fault diagnosis of micro electromechanical system (MEMS) gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The number of hidden layer nodes and parameters of RBF neural network are obtained by using OSCA. The connection weights are encoded to generate the chromosome, which is operated by adaptive GA. Orthogonal least square algorithm (OLS) is used to train the weights and gradient descent algorithm (GDA) with momentum term is used to estimate the parameters of Gaussian function. Adaptive GA, OLS and GDA with momentum term iterate alternately. Experimental results show that the proposed GA-based RBF neural network with OSCA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

Original language	English
Title of host publication	IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management
Pages	2348-2352
Number of pages	5
DOIs	https://doi.org/10.1109/IEEM.2009.5373007
Publication status	Published - 2009
Event	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 - Hong Kong, China Duration: 8 Dec 2009 → 11 Dec 2009

Publication series

Name	IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management

Conference

Conference	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009
Country/Territory	China
City	Hong Kong
Period	8/12/09 → 11/12/09

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Fault diagnosis
Genetic algorithm
Optimal selection clustering algorithm
Radial basis function neural network
Strapdown inertial navigation system

Access to Document

10.1109/IEEM.2009.5373007

Cite this

Liu, Z., Chen, J., Han, Y., & Song, C. (2009). A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis. In IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management (pp. 2348-2352). Article 5373007 (IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management). https://doi.org/10.1109/IEEM.2009.5373007

Liu, Zhide ; Chen, Jiabin ; Han, Yongqiang et al. / A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis. IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management. 2009. pp. 2348-2352 (IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management).

@inproceedings{55bbc338100b41638d353aeba7ba4d2d,

title = "A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis",

abstract = "In this paper, a new adaptive genetic algorithm (GA)-based radial basis function (RBF) neural network with optimal selection clustering algorithm (OSCA) is proposed for the fault diagnosis of micro electromechanical system (MEMS) gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The number of hidden layer nodes and parameters of RBF neural network are obtained by using OSCA. The connection weights are encoded to generate the chromosome, which is operated by adaptive GA. Orthogonal least square algorithm (OLS) is used to train the weights and gradient descent algorithm (GDA) with momentum term is used to estimate the parameters of Gaussian function. Adaptive GA, OLS and GDA with momentum term iterate alternately. Experimental results show that the proposed GA-based RBF neural network with OSCA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.",

keywords = "Fault diagnosis, Genetic algorithm, Optimal selection clustering algorithm, Radial basis function neural network, Strapdown inertial navigation system",

author = "Zhide Liu and Jiabin Chen and Yongqiang Han and Chunlei Song",

year = "2009",

doi = "10.1109/IEEM.2009.5373007",

language = "English",

isbn = "9781424448708",

series = "IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management",

pages = "2348--2352",

booktitle = "IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management",

note = "IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 ; Conference date: 08-12-2009 Through 11-12-2009",

}

Liu, Z, Chen, J , Han, Y & Song, C 2009, A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis. in IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management., 5373007, IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management, pp. 2348-2352, IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009, Hong Kong, China, 8/12/09. https://doi.org/10.1109/IEEM.2009.5373007

A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis. / Liu, Zhide; Chen, Jiabin ; Han, Yongqiang et al.
IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management. 2009. p. 2348-2352 5373007 (IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis

AU - Liu, Zhide

AU - Chen, Jiabin

AU - Han, Yongqiang

AU - Song, Chunlei

PY - 2009

Y1 - 2009

N2 - In this paper, a new adaptive genetic algorithm (GA)-based radial basis function (RBF) neural network with optimal selection clustering algorithm (OSCA) is proposed for the fault diagnosis of micro electromechanical system (MEMS) gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The number of hidden layer nodes and parameters of RBF neural network are obtained by using OSCA. The connection weights are encoded to generate the chromosome, which is operated by adaptive GA. Orthogonal least square algorithm (OLS) is used to train the weights and gradient descent algorithm (GDA) with momentum term is used to estimate the parameters of Gaussian function. Adaptive GA, OLS and GDA with momentum term iterate alternately. Experimental results show that the proposed GA-based RBF neural network with OSCA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

AB - In this paper, a new adaptive genetic algorithm (GA)-based radial basis function (RBF) neural network with optimal selection clustering algorithm (OSCA) is proposed for the fault diagnosis of micro electromechanical system (MEMS) gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The number of hidden layer nodes and parameters of RBF neural network are obtained by using OSCA. The connection weights are encoded to generate the chromosome, which is operated by adaptive GA. Orthogonal least square algorithm (OLS) is used to train the weights and gradient descent algorithm (GDA) with momentum term is used to estimate the parameters of Gaussian function. Adaptive GA, OLS and GDA with momentum term iterate alternately. Experimental results show that the proposed GA-based RBF neural network with OSCA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

KW - Fault diagnosis

KW - Genetic algorithm

KW - Optimal selection clustering algorithm

KW - Radial basis function neural network

KW - Strapdown inertial navigation system

UR - https://www.scopus.com/pages/publications/77949533319

U2 - 10.1109/IEEM.2009.5373007

DO - 10.1109/IEEM.2009.5373007

M3 - Conference contribution

AN - SCOPUS:77949533319

SN - 9781424448708

T3 - IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management

SP - 2348

EP - 2352

BT - IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management

T2 - IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009

Y2 - 8 December 2009 through 11 December 2009

ER -

Liu Z, Chen J , Han Y , Song C. A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis. In IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management. 2009. p. 2348-2352. 5373007. (IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management). doi: 10.1109/IEEM.2009.5373007

A new GA-based RBF neural network with optimal selection clustering algorithm for SINS fault diagnosis

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this