Abstract
A kind of fuzzy inference modeling method based on T-S fuzzy system is proposed. New input-output models and state-space models are constructed respectively by applying this method to time-invariant second-order freedom movement systems modeling. The obtained differential equation models are used to simulate the second-order equations, and the results show that the models achieve a good approximation precision.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang L.X.: A Course in Fuzzy Systems and Control. Tsinghua University Press, Beijing (2003)
Yuan, X.H., Liu, Z.L., Lee, E.S.: Center-of-gravity fuzzy systems based on normal fuzzy implications. Comput. Math. Appl. 61(9), 2879–2898 (2011)
Li, Y.M., Shi, Z.K., Li, Z.H.: Approximation theory of fuzzy systems based upon genuine many-valued implications-SISO cases. Fuzzy Sets Syst. 130(2), 147–157 (2002)
Zeng, X.J., Madan, G.S.: Approximation theory of fuzzy systems-SISO case. IEEE Trans. Fuzzy Syst. 2(2), 162–176 (1994)
Wang, J.Y., Liu, M., Li, H.X.: Analysis of difference between control function and interpolation expression of SISO fuzzy controller. Acta Electronica Sin. 37(2), 424–428 (2009)
Shan, W.W., Jin, D.M., Liang, Y.: Variable universe adaptive fuzzy logic controller CMOS analog chip implementation. Acta Electronica Sin. 37(5), 913–917 (2009)
Yuan, X.H., Li, H.X., Sun, K.B.: Fuzzy Systems and their approximation capability based on parameter singleton fuzzifier methods. Acta Electronica Sin. 39(10), 2372–2377 (2011)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning I. Inform. Sci. 8(3), 199–249 (1975)
Wang, G.J.: Non-Classical Logic and Approximate Reasoning. Science Press, Beijing (2000)
Li, H.X.: Interpolation mechanism of fuzzy control. Sci. China (Series E) 41(3), 312–320 (1998)
Li, H.X., Wang, J.Y., Miao, Z.H.: Modeling on fuzzy control systems. Sci. China (Series A) 12(45), 1506–1517 (2002)
Li, H.X., Song, W.Y., Yuan, X.H., Li, Y.C.: Time-varying system modeling method based on fuzzy inference. J. Syst. Sci. Math. Sci. 29(8), 1109–1128 (2009)
Yuan, X.H., Li, H.X., Yang, X.: Fuzzy system and fuzzy inference modeling method based on fuzzy transformation. Acta Electronica Sin. 41(4), 674–680 (2013)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)
Ban, X.J., Gao, X.Z., Huang, X.L., Vasilakos, A.V.: Stability analysis of the simplest Takagi-sugeno fuzzy control system using circle criterion. Inform. Sci. 177(20), 4387–4409 (2007)
Liu, P.Y., Li, H.X.: Hierarchical T-S fuzzy system and its universal approximation. Inform. Sci. 169(3), 279–303 (2005)
Chen, X.J.: Research on TS fuzzy Model Based Simulation of Double Inverted Pendulum System. Hefei University of Technology, Hefei (2005)
Acknowledgments
Thanks to the support by National Science Foundation of China (No. 90818025 and No. 61074044).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Jiang, Mz., Zhang, Cl., Yuan, Xh., Li, Hx. (2016). Fuzzy Inference Modeling Method Based on T-S Fuzzy System. In: Cao, BY., Liu, ZL., Zhong, YB., Mi, HH. (eds) Fuzzy Systems & Operations Research and Management. Advances in Intelligent Systems and Computing, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-319-19105-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-19105-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19104-1
Online ISBN: 978-3-319-19105-8
eBook Packages: EngineeringEngineering (R0)