[1] Zhu Hongxia, Shen Jiong, Su Zhigang, et al. RBF neural network regression modelbased on fuzzy observations [J]. Journal of Southeast University (English Edition), 2013, 29 (4): 400-406. [doi:10.3969/j.issn.1003-7985.2013.04.009]

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RBF neural network regression modelbased on fuzzy observations()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 4

Page:

400-406

Research Field:

Automation

Publishing date:

2013-12-20

Info

Title:

RBF neural network regression modelbased on fuzzy observations

Author(s):

Zhu Hongxia¹;  2;  Shen Jiong¹;  Su Zhigang¹

¹School of Energy and Environment, Southeast University, Nanjing 210096, China
²School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Keywords:

radial basis function neural network(RBFNN);  fuzzy membership function;  imprecise observation;  regression model

PACS:

TP183

DOI:

10.3969/j.issn.1003-7985.2013.04.009

Abstract:

A fuzzy observations-based radial basis function neural network(FORBFNN)is presented for modeling nonlinear systems in which the observations of response are imprecise but can be represented as fuzzy membership functions. In the FORBFNN model, the weight coefficients of nodes in the hidden layer are identified by using the fuzzy expectation-maximization(EM)algorithm, whereas the optimal number of these nodes as well as the centers and widths of radial basis functions are automatically constructed by using a data-driven method. Namely, the method starts with an initial node, and then a new node is added in a hidden layer according to some rules. This procedure is not terminated until the model meets the preset requirements. The method considers both the accuracy and complexity of the model. Numerical simulation results show that the modeling method is effective, and the established model has high prediction accuracy.

References:

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[3] Li Y, Qiang S, Zhuang X, et al. Robust and adaptive backstepping control for nonlinear systems using RBF neural networks[J]. IEEE Transactions on Neural Networks, 2004, 15(3): 693-701.
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Memo

Memo:

Biographies: Zhu Hongxia(1980—), female, graduate; Shen Jiong(corresponding author), male, doctor, professor, shenj@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.51106025, 51106027, 51036002), Specialized Research Fund for the Doctoral Program of Higher Education(No.20130092110061), the Youth Foundation of Nanjing Institute of Technology(No.QKJA201303).
Citation: Zhu Hongxia, Shen Jiong, Su Zhigang. RBF neural network regression model based on fuzzy observations[J].Journal of Southeast University(English Edition), 2013, 29(4):400-406.[doi:10.3969/j.issn.1003-7985.2013.04.009]

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