Designing a Soft Sensor with the Weighted Fuzzy Neural Network

Abstract:

Soft sensors are algorithms capable of estimate the process output that can not be measured directly in real time. A data-driven soft sensor is an inferential model developed from process observations. In this paper, the soft sensor modeling process based on the weighted fuzzy neural network was discussed. The proposed algorithm based on genetic algorithm and particle swarm optimization could obtain a near-optimal structure of fuzzy neural network, and the numerical experiments show that the soft sensor model has good performance.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 472-475
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.472
• Online since: December 2012
• Authors: Yan Sun, Ming Ma
• Keywords: Fuzzy Neural Network (FNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Soft Sensor

References

[1] Bhartiya, S., Whiteley, J.R.: Development of Inferential measurements Using Neural Networks. ISA Transactions, Vol 40(4), 307–323 (2001)

DOI: 10.1016/s0019-0578(01)00004-0

[2] Yan, W.W., Shao, H.H., Wan, X.F.: Soft sensing modeling based on support vector machine and Bayesian model selection. Computers and Chemical Engineering, Vol 28(8), 1489–1498 (2004)

DOI: 10.1016/j.compchemeng.2003.11.004

[3] Chen, W., Li, J.M.: Adaptive Output-feedback Regulation for Nonlinear Delayed Systems Using Neural Network. International Journal of Automation and Computing, Vol 5(1), 103–108 (2008)

DOI: 10.1007/s11633-008-0103-2

[4] T-Sekouras, G., Sarimveis, H., Kavakli, E., Bafas, G.: A hierarchical fuzzy clustering approach to fuzzy modeling. Fuzzy Sets and Systems, Vol 150, 245–266 (2005)

DOI: 10.1016/j.fss.2004.04.013

[5] SM Zhou and JQ Gan:Low-level interpretability and high-level interpretability: A unified view of data-driven interpretable fuzzy system modeling. Fuzzy Sets and Systems, Vol 159(23), 3091-3131(2008 )

DOI: 10.1016/j.fss.2008.05.016

[6] MJ Gacto, R. Alcalá and F. Herrera:Integration of an Index to Preserve the Semantic Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems.IEEE Transactions on Fuzzy Systems, Vol 18(3), 515-531(2010)

DOI: 10.1109/tfuzz.2010.2041008

[7] CG Zhou, YC Liang,: A Study of Identif Ication of Taste Signals Based on Fuzzy Neural Networks. Chinese Journal of Computer Research & Development, Vol.36 (14), 401-409(1999)

[8] Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In:Proc. Sixth International Symposium on Micro Machine and Human Science(Nagoya, Japan). IEEE Service Center,Piscataway,NJ (1995) 39-43

DOI: 10.1109/mhs.1995.494215

[9] Kennedy, J., Eberhart, R.: Particle Swarm Optimization.In: IEEE International Conference on Neural Networks (Perth, Australia). IEEE Service Center, Piscataway, NJ (1995), Volume IV, 1942-1948