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This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules.



Neuro-fuzzy systems Fuzzy clustering Takagi-Sugeno modeling Nonlinear systems

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How to Cite
Al-Gallaf, E. A. (2005). Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System. The Journal of Engineering Research [TJER], 2(1), 12–24.


  1. Akkizidis, S. and Roberts, N., 2001, "Fuzzy Clustering Methods for Identifying and Modeling of Non-Linear Control Strategies": Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, Vol. l(215), pp. 437-452.
  2. Babuska, R. and Verburggen, H., 1995, "Identification of Composite Linear Models via Fuzzy Clustering," Proceedings of the European Control Conference, Italy, pp. 1207-1212.
  3. Bologna, G., 2001, "A New Neuro-Fuzzy Model," Proceedings of the International Joint Conference on Neural Networks (IJCNN'01), Vol. 2, Washington-DC (USA), pp. 1328-1333.
  4. Bossley, K., 1997, "Neuro-Fuzzy Modeling Approaches in System Identification," Ph.D. Thesis, University of Southampton, U.K.
  5. Gorzalczany, E., Marian, B. and Gluszek, A., 2000, "Neuro-Fuzzy Networks in Time Series Modeling," Proceedings of the International Conference on Knowledge-Based Intelligent Electronic Systems, Vol.
  6. , Brighton, (UK), pp. 450-453.
  7. Ikonen, E., Najim, K. and Kortela, U., 2000, "Neuro- Fuzzy Modelling of Power Plant Flue-Gas Emissions," J. of Engineering Applications of Artificial Intelligence, Vol. 3(6), pp. 705-717.
  8. Lo, J. and Chen, Y., 1999, "Stability Issues on Takagi- Sugeno Fuzzy Model, Parametric Approach", IEEE Transaction of Fuzzy Systems, Vol. 7(5), pp. 597-607.
  9. Mamdani, E. and Assilian, S., 1975, "An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller," International J. of Man-Machine Studies, Vol. 3(5), pp. 1-13.
  10. Min-You, Chen. and Linkens, A., 1998, "Fast Fuzzy Modeling Approach Using Clustering Neural Networks," IEEE International Conference on Fuzzy Systems, Vol. l(2), IEEE World Congress on Computational Intelligence, pp. 1088-1093.
  11. Ning, L., Shaoyuan, L. and Yugeng, X., 2001, "Modeling pH Neutralization Processes Using Fuzzy Satisfactory Clustering," IEEE International Conference on Fuzzy Systems, Vol. l(1), pp. 308-311.
  12. Takagi, T. and Sugeno, M., 1985, "Fuzzy Identification of Systems and its Application to Modeling and Control," IEEE Transaction Systems, Man and Cybernetics, Vol. 15(1), pp. 116-132.
  13. Wang, H., Li, J., Niemann, D. and Tanaka, K., 2000, "TS Fuzzy Model with Linear Rule Consequence andPDC Controller: AUniversal Framework for Nonlinear Control Systems," Proceedings of the 9th IEEE International Conference on Fuzzy Systems. Zhang, J. and Knoll, A., 1999, "Modelling Multivariate Data by Neuro-Fuzzy Systems", Proceedings of the 1999 IEEE/IAE Conference on Computational Intelligence for Financial Engineering (CIFEr), New York, (USA), pp. 267-270.