A Revised Clustering Algorithm Based RBF Neural Network Approach for Modeling of an Electro-Hydraulic Servo System

Ji Lin Chen; Jin Qian; Zhong Zhi Tong; Yuan Long Hou

doi:10.4028/www.scientific.net/AMM.157-158.1595

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 157-158A Revised Clustering Algorithm Based RBF Neural...

A Revised Clustering Algorithm Based RBF Neural Network Approach for Modeling of an Electro-Hydraulic Servo System

Abstract:

The paper presents an approach to model the electro-hydraulic system of a certain explosive mine sweeping device using the Radial Basis Function (RBF) neural network. In order to obtain accurate and simple RBF neural network, a revised clustering method is used to train the hidden node centers of the neural network, in which the subtractive clustering(SC) algorithm was used to determine the initial centers and the fuzzy C – Means(FCM) clustering algorithm to further determined the centers data set. The spread factors and the weights of the neural network are calculated by the modified recursive least squares (MRLS) algorithm for relieving computational burden. The proposed algorithm is verified by its application to the modeling of an electro-hydraulic system, simulation and experiment results clearly indicate the obtained RBF network can model the electro-hydraulic system satisfactorily and comparison results also show that the proposed algorithm performs better than the other methods.

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Periodical:

Applied Mechanics and Materials (Volumes 157-158)

Pages:

1595-1600

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.157-158.1595

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Online since:

February 2012

Authors:

Ji Lin Chen, Jin Qian, Zhong Zhi Tong, Yuan Long Hou

Keywords:

Clustering Algorithm, Explosive Sweeping Mine Device, Modeling, Radial Basis Function Neural Network (RBFNN), Recursive Least Squares Algorithm

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References

[1] LI We, FENG Ke, LIU Fa-heng, ZENG Yong-hua. Research on Self-Control Behavior Simulation of Integrated Mine Clearing Vehicle [J]. Engineer Equipment Research. 2005, 24(5), 42-45.

Google Scholar

[2] Zhang Yuan, Xing Zongyi, Gao Qiang, Qin Yong, Jia Limin. Modeling of Mine Sweeping Plough Using Hierarchical Genetic Neural Networks [J]. China Mechanical Engineering. 2010, 21(1), 89-93.

Google Scholar

[3] Massimo Panella, Antonio Stanislao Gallo. An Input-Output Clustering Approach to the Synthesis of ANFIS Networks[J]. IEEE Transactions On Fuzzy Systems, 2005, 13(1): 69-81.

DOI: 10.1109/tfuzz.2004.839659

Google Scholar