Abstract
In order to implement real-time control or optimization for variables key to the process, we need to build soft sensor, and the key step of it is soft sensor modeling. In this paper, the soft sensor modeling process based on Takagi-Sugeno (T-S) model and Differential Evolution (DE) were discussed. The proposed algorithm could evolve both the structure of T-S model and parameters, and effectively solves the problem of soft sensor modeling. The numerical experiments indicate the effectiveness of the algorithm.
Keywords
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References
Martin, G.: Consider soft sensors. Chemical Engineering Progress 66(7), 66–70 (1997)
Bhartiya, S., Whiteley, J.R.: Development of Inferential measurements Using Neural Networks. ISA Transactions 40(4), 307–323 (2001)
Chen, W., Li, J.M.: Adaptive Output-feedback Regulation for Nonlinear Delayed Systems Using Neural Network. International Journal of Automation and Computing 5(1), 103–108 (2008)
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 28(8), 1489–1498 (2004)
Zhang, Y., Su, H.Y., Liu, R.L., Chu, J.: Fuzzy Support Vector Regression Model of 4-CBA Concentration for Industrial PTA Oxidation Process. Chinese J. Chem. Eng. 13(5), 642–648 (2005)
Setnes, M., Roubos, H.: GA-fuzzy modeling and classification: complexity and performance. IEEE Trans. Fuzzy Systems 8(5), 509–522 (2000)
Mastorocostas, P.A., Theocharis, J.B., Petridis, V.S.: A constrained orthogonal least-squares method for generating TSK fuzzy models: application to short-term load forecasting. Fuzzy Sets and Systems 118(2), 215–233 (2001)
Xing, Z.Y., Jia, L.M., Yong, Z.: A Case study of data-driven interpretable fuzzy modeling. Acta Automatica Sinica 31(6), 815–824 (2005)
T-Sekouras, G., Sarimveis, H., Kavakli, E., Bafas, G.: A hierarchical fuzzy clustering approach to fuzzy modeling. Fuzzy Sets and Systems 150, 245–266 (2005)
Takagi, T., Sugeno, M.: Fuzzy identification of system s and its app lication to modeling and control. IEEE Trans. on Systems, Man and Cybernetics 15(1), 116–132 (1985)
Storn, R., Price, K.: Differential Evolution - A Simple and Efficient Heuristic Strategy for Global Optimization over Continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)
Vesterstrom, J., Thomsen, R.: A Comparative Study of Differential Evolution. Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems. Evolutionary Computation 2, 1980–1987 (2004)
Storn, R.: System Design by Constraint Adaptation and Differential Evolution. IEEE Transactions on Evolutionary Computation 2, 82–102 (1999)
Yang, Z.Y., Tang, K., Yao, X.: Self-adaptive differential evolution with neighborhood search. In: Proc. of 2008 IEEE Congress on Evolutionary Computation, pp. 1110–1116 (2008)
Das, S., Abraham, A., Konar, A.: Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 38(1), 218–237 (2008)
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Sun, Y., Zhang, LB., Ma, M. (2012). Soft Sensor Modeling Based on Fuzzy System Optimization. In: Cao, BY., Xie, XJ. (eds) Fuzzy Engineering and Operations Research. Advances in Intelligent and Soft Computing, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28592-9_10
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DOI: https://doi.org/10.1007/978-3-642-28592-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28591-2
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