Soft Sensor Modeling Based on Radial Basis Function Neural Network and Fuzzy C-Means

Xi Huai Wang; Jian Mei Xiao

doi:10.4028/www.scientific.net/AMR.219-220.1263

Paper Titles

Developing the Balanced Scorecard Strategies for Vocational College Library
p.1243

Reconstruction Technology on the Surface of Engine Port Based on Reverse Engineering
p.1249

Assessment Analysis of Financial Stability in Central China Considering the Asset Bubble Situation — Based on the Empirical Analysis of Financial Sector in Hubei Province
p.1253

The Research on Nonlinear Diagnosis Methods of Vibrating Screen Crack
p.1258

Soft Sensor Modeling Based on Radial Basis Function Neural Network and Fuzzy C-Means
p.1263

Development of a Decision Support System for Flood Warning in Jinan City
p.1267

Design and Realization of Smart Home Control System Based on S3C2440
p.1271

Research on Personalized Recommendation Technology for Tourism Industry - A Perspective of a System Framework Design
p.1276

The Construction of the Enterprise Culture after Financial Crisis
p.1281

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 219-220Soft Sensor Modeling Based on Radial Basis...

Soft Sensor Modeling Based on Radial Basis Function Neural Network and Fuzzy C-Means

Abstract:

A neural network soft sensor based on fuzzy clustering is presented. The training data set is separated into several clusters with different centers, the number of fuzzy cluster is decided automatically, and the clustering centers are modified using an adaptive fuzzy clustering algorithm in the online stage. The proposed approach has been applied to the slab temperature estimation in a practical walking beam reheating furnace. Simulation results show that the approach is effective.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 219-220)

Pages:

1263-1266

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.219-220.1263

Citation:

Cite this paper

Online since:

March 2011

Authors:

Xi Huai Wang, Jian Mei Xiao

Keywords:

Fuzzy C-Means (FCM), Modeling, Radial Basis Function Neural Network (RBFNN), Reheating Furnace, Soft Sensor

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Fortuna, L., Graziani, S., and Xibilia, M.G., Comparison of Soft-Sensor Design Methods for Industrial Plants Using Small Data Sets, IEEE Transactions on Instrumentation and Measurement, vol.58 (2009), pp.2444-2451.

DOI: 10.1109/tim.2009.2016386

Google Scholar

[2] Ying-Xin Liao, Jin-Hua She, and Min Wu, Integrated Hybrid-PSO and Fuzzy-NN Decoupling Control for Temperature of Reheating Furnace, IEEE Transactions on Industrial Electronics, vol.56 (2009), 2704-2714.

DOI: 10.1109/tie.2009.2019753

Google Scholar

[3] Young Il Kim, Ki Cheol Moon, Byoung Sam Kang, Chonghun Han, and Kun Soo Chang, Application of neural network to the supervisory control of a reheating furnace in the steel industry, Control Engineering Practice(1998), no. 6, pp.1009-1014.

DOI: 10.1016/s0967-0661(98)00098-7

Google Scholar

[4] Chang, G.W., Cheng-I Chen, and Yu-Feng Teng, Radial-Basis-Function-Based Neural Network for Harmonic Detection, IEEE Transactions on Industrial Electronics, vol.57 (2010),pp.2171-2179.

DOI: 10.1109/tie.2009.2034681

Google Scholar

[5] Biswal, B., Dash, P.K., and Panigrahi, B.K., Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization, IEEE Transactions on Industrial Electronics, vol. 56, (2009), pp.212-220.

DOI: 10.1109/tie.2008.928111

Google Scholar

[6] Li Ning, Wang Xihuai, Li Shaoyuan, and Xi Yugeng, The Design of simulation system for reheating furnaces modeling and optimization, Journal of System Simulation (2001), vol. 13, pp.361-363.

Google Scholar