Sains Malaysiana 40(6)(2011): 643–650

 

Incorporating Optimisation Technique into Zadeh’s Extension Principle for Computing Non-Monotone Functions with Fuzzy Variable

(Menggabungkan Teknik Pengoptimuman ke dalam Prinsip Perluasan Zadeh untuk Komputeran Fungsi-Fungsi Tak Bermonoton dengan Pembolehubah Kabur)

 

M. Z. Ahmad*

Institute for Engineering Mathematics, Universiti Malaysia Perlis, 02000 Kuala Perlis, Perlis, Malaysia

 

M. K. Hasan

School of Information Technology, Faculty of Technology and Information Science, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

 

Received: 23 February / Accepted: 1 June 2010

 

ABSTRACT

 

This paper proposes a new computational method for computing non-monotone functions that take a fuzzy interval as their arguments. The proposed method represents an implementation of optimisation technique into Zadeh’s extension principle. By taking into account the dependency problem that exists in fuzzy environment, the proposed method can avoid the effect of overestimation in computation. This problem usually arises when the same fuzzy interval is computed separately in fuzzy interval computation. The proposed method is simple to use and can be implemented in many practical applications. In order to show the capability of the proposed method, several non-monotone functions with trapezoidal fuzzy intervals are studied.

 

Keywords: Fuzzy set; optimisation; Zadeh’s extension principle

 

ABSTRAK

 

Makalah ini mencadangkan satu kaedah komputasi baru untuk komputeran fungsi-fungsi tak bermonoton yang mengambil selang kabur sebagai pembolehubahnya. Kaedah yang dicadangkan merupakan suatu perlaksanaan teknik pengoptimuman di dalam prinsip perluasan Zadeh. Dengan mengambilkira masalah kebergantungan yang wujud di dalam persekitaran kabur, kaedah yang dicadangkan ini dapat mengelakkan masalah terlebih anggaran dalam pengiraan. Masalah ini biasanya wujud apabila selang kabur yang sama dikira secara berasingan di dalam komputasi selang kabur. Kaedah ini mudah untuk dilaksanakan dan dapat diterapkan di dalam pelbagai penggunaan praktikal. Untuk menunjukkan kebolehan kaedah yang dicadangkan, beberapa fungsi tak bermonoton dengan selang kabur trapezoid dikaji.

 

Kata kunci: Pengoptimuman; prinsip perluasan Zadeh; set kabur

 

REFERENCES

 

Abbasbandy, S. & Allahviranloo, T. 2002. Numerical solutions of fuzzy differential equations by Taylor method. Computational Methods in Applied Mathematics 2: 113-124.

Abbasbandy, S. & Allahviranloo, T. 2004. Numerical solution of fuzzy differential equation by Runge-Kutta method. Nonlinear Studies 11: 117-129.

Ahmad, M.Z. & Hasan, M.K. 2010. Incorporating Optimisation Technique into Euler’s Method for Solving Differential Equations with Fuzzy Initial Values. Proceeding of the 1st Regional Conference on Applied and Engineering Mathematics: 2 – 3 June 2010, Penang, Malaysia.

Brent, R.P. 2002. Algorithms for Minimization without Derivatives. New Jersey: Prentice-Hall.

Chalco-Cano, Y., Mizukoshi, M.T., Román-Flores, H. & Flores-Franulic, A. 2009. Spline approximation for Zadeh’s extension. Int. J. Uncertainty Fuzziness Knowledge-Based Systems 17: 269-280.

Dong, W.M. & Wong, F.S. 1987. Fuzzy weighted average and implementation of the extension principle. Fuzzy Sets and Systems 21: 183-199.

Dong, W.M. & Shah, H.C. 1987. Vertex method for computing functions of fuzzy variables. Fuzzy Sets and Systems 24: 65-78.

Hanss, M. 2002. The transformation method for the simulation and analysis of systems with uncertain parameters. Fuzzy Sets and Systems 130: 277-289.

Kaufmann, A. & Gupta, M.M. 1991. Introduction to Fuzzy Arithmetic: Theory and Application. New York: Van Nostrand Reinhold.

Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. 1983. Optimization by simulated annealing. Science, New Series 220: 671-680.

Klimke, A. 2003. An efficient implementation of the transformation method of fuzzy arithmetic. Proceeding of the 22nd International Conference of the North American, Fuzzy Information Processing Society, 468-473.

Klir, G. J. 1997. Fuzzy arithmetic with requisite constraints. Fuzzy Sets and Systems 91: 165-175.

Ma, M., Friedman, M. & Kandel, A. 1999. Numerical solution of fuzzy differential equations. Fuzzy Sets and Systems 105: 133-138.

Moore, R.E. 1966. Interval Analysis. Prentice-Hall, Englewood Cliffs, N.J.

Palligkinis, S. Ch., Papageorgiou, G. & Famelis, I.Th. 2008. Runge-Kutta methods for fuzzy differential equations. Applied Mathematics and Computation 209: 97-105.

Pederson, S. & Sambandham, M. 2007. Numerical solution to hybrid fuzzy systems. Mathematical and Computer Modelling45: 1133-1144.

Pederson, S. & Sambandham, M. 2008. The Runge-Kutta method for hybrid fuzzy differential equations. Nonlinear Analysis: Hybrid Systems 2: 626-634.

Press, W.H., Teukolsky, S.A., Vetterling, W. T. & Flannery, B.P. 2007. Numerical Recipes: the Art of Scientific Computing. 3rd Edition. Cambridge: Cambridge University Press.

Román-Flores, H., Barros, L.C. & Bassanezi, R. 2001. A note on Zadeh’s extension principle. Fuzzy Sets and Systems 17: 327-331.

Wood, K.L., Otto, K.N. & Antonsson, E.K. 1992. Engineering design calculation with fuzzy parameters. Fuzzy Sets and Systems 52: 1-20.

Yang, H.Q., Yao, H. & Jones, J.D. 1993. Calculating functions on fuzzy numbers. Fuzzy Sets and Systems 55: 273-283.

Zadeh, L.A. 1965. Fuzzy sets. Information and Control 8: 338-353.

Zadeh, L.A. 1975a. The concept of linguistic variables and its application to approximate reasoning I, Information Sciences 8: 199-249.

Zadeh, L.A. 1975b. The concept of linguistic variables and its application to approximate reasoning II, Information Sciences 8: 301-357.

Zadeh, L.A. 1975c. The concept of linguistic variables and its application to approximate reasoning III, Information Sciences 9: 43-80.

 

*Corresponding author; email: mzaini@unimap.edu.my

 

 

 

previous