Fuzzy Relational Dynamic System with Smooth Fuzzy Composition

Volume 2, Issue 1, pp 1--8 http://dx.doi.org/10.22436/jmcs.002.01.01

Download PDF

Download XML

• Export citations
  • CITATIONS & REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • ARTICLE CITATION
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)
  • REFERENCES
  • EndNote (.ENW)
  • Mendeley, JabRef (.BIB)
  • Papers, Zotero, Reference Manager, RefWorks (.RIS)

• 2251 Downloads
• 3517 Views

Authors

Arya Aghili Ashtiani - Amirkabir University of Technology Mohammad Bagher Menhaj - Amirkabir University of Technology

Abstract

Fuzzy relational models of functions have been developed in recent two decades which has led to fuzzy relational models of dynamic systems which we call fuzzy relational dynamic systems (FRDS). In this paper the effectiveness of smooth fuzzy relational compositions (FRC) in such dynamic models is studied after introducing a general framework for modeling of dynamic systems using FRDS, and so the smooth FRDS is developed. A modeling structure is presented in this regard as well as a related identification algorithm. Finally, the modeling capability of the proposed smooth FRDS is verified via some simulations on various benchmark problems and actual dynamic systems.

Share and Cite

Keywords

• Fuzzy relational dynamic system
• smooth fuzzy relational composition
• fuzzy relational modeling.

MSC

•  03B52
•  93C42
•  08A02
•  37N35

References

• [1] E. Sanchez, Resolution of Composite Fuzzy Relation Equations, Information and Control, 30 (1976), 38--48
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [2] W. Pedrycz, An identification algorithm in fuzzy relational systems, Fuzzy Sets and Systems, 13 (1984), 153--167
  • View Article
  • Google Scholar
  • MATH


• [3] W. Pedrycz, Fuzzy neural networks and neurocomputations, Fuzzy Sets and Systems, 56 (1993), 1--28
  • View Article
  • Google Scholar


• [4] J. N. Ridley, I. S. Shaw, J. J. Kruger, Probabilistic fuzzy model for dynamic systems, Electronics Letters, 24 (1988), 890--892
  • View Article
  • Google Scholar
  • MATH


• [5] A. Aghili Ashtiani, M. B. Menhaj, Numerical Solution of Fuzzy Relational Equations Based on Smooth Fuzzy Norms, Soft Comput., 14 (2010), 545--557
  • View Article
  • Google Scholar
  • MATH


• [6] V. M. Box, G. M. Jenkins, Time Series Analysis: Forecasting and control, https://scholar.google.com/scholar?as_q=&as_epq=Time+Series+Analysis&as_oq=&as_eq=&as_occt=title&as_sauthors=&as_publication=&as_ylo=1970&as_yhi=1970&hl=en&as_sdt=0%2C5, San Francisco (1970)
  • View Article
  • MathSciNet
  • Google Scholar


• [7] R. M. Tong, The evaluation of fuzzy models derived from experimental data, Fuzzy Sets and Systems, 4 (1980), 1--12
  • View Article
  • Google Scholar
  • MATH


• [8] Y. C. Lee, C. Hwang, Y. P. Shih, A Combined Approach to Fuzzy Model Identification, IEEE Trans. Systems Man Cybernet., 24 (1994), 736--744
  • View Article
  • MathSciNet
  • Google Scholar
  • MATH


• [9] C. W. Xu, Y. Z. Lu, Fuzzy model identification and self-learning for dynamic systems, IEEE Trans. Syst. Man Cybern., 17 (1987), 683--689
  • View Article
  • Google Scholar
  • MATH


• [10] P. J. Costa Branco, J. A. Dente, A New Algorithm for On-Line Relational Identification of Nonlinear Dynamic Systems, Proceedings Second IEEE International Conference on Fuzzy Systems, 1993 (1993), 1173--1178
  • View Article
  • Google Scholar