A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis

Zhide Liu; Jiabin Chen; Chunlei Song

doi:10.1109/CCDC.2009.5195114

A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis

Zhide Liu^*, Jiabin Chen, 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

10 Citations (Scopus)

Abstract

In this paper, a new radial basis function (RBF) neural network with fuzzy c-means clustering algorithm based on genetic algorithm (GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The fuzzy c-means algorithm (FCM) tends to fall into the local optimum. The fuzzy c-means clustering algorithm combined with GA (FGA) obtains the global optimal cluster centers. FGA is used to provide the optimal cluster centers for RBF neural network, and a second order learning algorithm is used to train the parameters and weights of RBF neural network. Experimental results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

Original language	English
Title of host publication	2009 Chinese Control and Decision Conference, CCDC 2009
Pages	208-211
Number of pages	4
DOIs	https://doi.org/10.1109/CCDC.2009.5195114
Publication status	Published - 2009
Event	2009 Chinese Control and Decision Conference, CCDC 2009 - Guilin, China Duration: 17 Jun 2009 → 19 Jun 2009

Publication series

Name	2009 Chinese Control and Decision Conference, CCDC 2009

Conference

Conference	2009 Chinese Control and Decision Conference, CCDC 2009
Country/Territory	China
City	Guilin
Period	17/06/09 → 19/06/09

Keywords

Fault diagnosis
Fuzzy c-means clustering algorithm
Genetic algorithm
Radial basis function neural network
Strapdown inertial navigation system

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10.1109/CCDC.2009.5195114

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@inproceedings{513080700b4d495a8882e31aff7ba7cd,

title = "A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis",

abstract = "In this paper, a new radial basis function (RBF) neural network with fuzzy c-means clustering algorithm based on genetic algorithm (GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The fuzzy c-means algorithm (FCM) tends to fall into the local optimum. The fuzzy c-means clustering algorithm combined with GA (FGA) obtains the global optimal cluster centers. FGA is used to provide the optimal cluster centers for RBF neural network, and a second order learning algorithm is used to train the parameters and weights of RBF neural network. Experimental results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.",

keywords = "Fault diagnosis, Fuzzy c-means clustering algorithm, Genetic algorithm, Radial basis function neural network, Strapdown inertial navigation system",

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

year = "2009",

doi = "10.1109/CCDC.2009.5195114",

language = "English",

isbn = "9781424427239",

series = "2009 Chinese Control and Decision Conference, CCDC 2009",

pages = "208--211",

booktitle = "2009 Chinese Control and Decision Conference, CCDC 2009",

note = "2009 Chinese Control and Decision Conference, CCDC 2009 ; Conference date: 17-06-2009 Through 19-06-2009",

}

Liu, Z, Chen, J & Song, C 2009, A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis. in 2009 Chinese Control and Decision Conference, CCDC 2009., 5195114, 2009 Chinese Control and Decision Conference, CCDC 2009, pp. 208-211, 2009 Chinese Control and Decision Conference, CCDC 2009, Guilin, China, 17/06/09. https://doi.org/10.1109/CCDC.2009.5195114

A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis. / Liu, Zhide; Chen, Jiabin ; Song, Chunlei.
2009 Chinese Control and Decision Conference, CCDC 2009. 2009. p. 208-211 5195114 (2009 Chinese Control and Decision Conference, CCDC 2009).

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

TY - GEN

T1 - A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis

AU - Liu, Zhide

AU - Chen, Jiabin

AU - Song, Chunlei

PY - 2009

Y1 - 2009

N2 - In this paper, a new radial basis function (RBF) neural network with fuzzy c-means clustering algorithm based on genetic algorithm (GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The fuzzy c-means algorithm (FCM) tends to fall into the local optimum. The fuzzy c-means clustering algorithm combined with GA (FGA) obtains the global optimal cluster centers. FGA is used to provide the optimal cluster centers for RBF neural network, and a second order learning algorithm is used to train the parameters and weights of RBF neural network. Experimental results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

AB - In this paper, a new radial basis function (RBF) neural network with fuzzy c-means clustering algorithm based on genetic algorithm (GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The fuzzy c-means algorithm (FCM) tends to fall into the local optimum. The fuzzy c-means clustering algorithm combined with GA (FGA) obtains the global optimal cluster centers. FGA is used to provide the optimal cluster centers for RBF neural network, and a second order learning algorithm is used to train the parameters and weights of RBF neural network. Experimental results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

KW - Fault diagnosis

KW - Fuzzy c-means clustering algorithm

KW - Genetic algorithm

KW - Radial basis function neural network

KW - Strapdown inertial navigation system

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U2 - 10.1109/CCDC.2009.5195114

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M3 - Conference contribution

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SN - 9781424427239

T3 - 2009 Chinese Control and Decision Conference, CCDC 2009

SP - 208

EP - 211

BT - 2009 Chinese Control and Decision Conference, CCDC 2009

T2 - 2009 Chinese Control and Decision Conference, CCDC 2009

Y2 - 17 June 2009 through 19 June 2009

ER -

A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis

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Keywords

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