An Efficient Approach based on Wu’s Method for Solving Fully Fuzzy Polynomial Equations System
الموضوعات : Fuzzy Optimization and Modeling JournalHamed Farahani 1 , Mohammad Javad Ebadi 2 , Seyed Ahmad Edalatpanah 3
1 - Chabahar Maritime University.
2 - Section of Mathematics, International Telematic University Uninettuno, Corso Vittorio Emanuele II, 39, 00186 Roma, Italy
3 - Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran
الکلمات المفتاحية: Fully Fuzzy Polynomial, Equations Systems, Fuzzy Numbers, Characteristic Sets, Wu’s Algorithm, Positive Solutions,
ملخص المقالة :
This article introduces a productive algebraic approach to identifying positive solutions for a system of fully fuzzy polynomial equations (FFPEs). To achieve this, the FFPEs system is transformed into a comparable system of crisp polynomial equations. The Wu's algorithm is then employed to solve the set of crisp polynomial equations as the solution method. This algorithm results in the solution of characteristic sets that are readily solvable. A key benefit of the proposed method is that all the solutions are obtained simultaneously. The article concludes by presenting some practical examples to demonstrate the efficacy of the proposed method.
1. Abbasbandy, S., & Otadi, M. (2007). Numerical solution of fuzzy polynomial equations by fuzzy coefficient method. Applied Mathematics and Computation , 190(2) , 1797–1803
2. Allahviranloo, T., Mikaeilvand, N., Kiani, & N. A., Shabestari, R. M., (2008). Signed decomposition of fully fuzzy linear systems. International journal of Applications and Applied Mathematics , 3 , 77–88.
3. Allahviranloo, T., Kiani, N. A., & Aghayari, R., (2011). A hybrid method for solving fuzzy polynomial equations. Soft Computing, 15(9) , 1745–1751.
4. Allahviranloo, T., Kiani, N. A., & Aghayari, R., (2013). Solving fuzzy polynomial equations by a new method. Soft Computing, 17(1) , 95–104.
5. Allahviranloo, T., Hosseinzadeh, A. A., Ghanbari, M., Hghi, E., & Nuraei, R., (2014). On the new solutions for a fully fuzzy linear system. Soft Computing, 18, 95–107.
6. Bardossy, A., & Duckstein, L., (1995). Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems., CRC Press.
7. Buckley, J., & Qu, Y., (1990). Solving linear and quadratic fuzzy equations. Fuzzy Sets and Systems , 35, 43–59.
8. Buckley, J., & Qu, Y., (1991). Solving fuzzy equations : a new solution concept. Fuzzy Sets and Systems , 39, 291–301.
9. Buckley, J., & Qu, Y., (1991). Solving systems of linear fuzzy equations. Fuzzy Sets and Systems , 43, 33–43.
10. Buckley, J. J., & Eslami, E., (2002). An introduction to fuzzy logic and fuzzy sets., Physica-Verlag HD.
11. Chou, S., Gao, X., & McPhee, N., (1989). A Combination of Ritt-Wu’s Method and Collins’ Method., Technical report, Austin, Texax, USA.
12. Cox, D., Little, J., & O’Shea, D., (2004). Using Algebraic Geometry, second edition. Springer-Varlag, New York.
13. Cox, D., J. Little, J., & O’Shea, D., (2007) Ideal, Varieties, and Algorithms: An introduction to computational algebra geometry and commutative algebra, third edition. Springer-Varlag, New York.
14. Dehghan, M., & Hashemi, B., (2006). Solution of the fully fuzzy linear systems using the decomposition procedure. Applied Mathematics and Computation , 182, 1568–1580.
15. Dehghan, M., Hashemi, B., & Ghatee, M., (2007) Solution of the fully fuzzy linear systems using iterative techniques computational. Chaos, Soutons and Fractalsn , 34, 316–336.
16. Dubois, D., & Prade, H., (1980). Fuzzy sets and systems: Theory and applications., Academic Press.
17. Farahani, H., Ebadi, M. J., & Jafari, H., (2019). Finding inverse of a fuzzy matrix using eigenvalue method. International Journal of innovative technology and exploring engineering, 9(2), 3030–3037.
18. Farahani, H., & Ebadi, M. J., (2019). Finding Fuzzy Inverse Matrix Using Wu’s Method. Journal of Mahani Mathematical Research Center, 10(1), 37–52.
19. Farahmand Nejad, M., Farahani, H., Nuraei, R., & Gilani, A., (2023). Gröbner Basis Approach for Solving Fuzzy Complex System of Linear Equations. New Mathematics and Natural Computation, DOI: 10.1142/S1793005724500297.
20. Ganesan, K., & Veeramani, P., (2013) Fuzzy multi-objective optimization using fuzzy goal programming. Journal of Intelligent & Fuzzy Systems , 25(1), 1–11.
21. Gao, X., Hou, X., Tang, J., & Cheng, H., (2003). Complete solution classification for the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. , 25, 930–943.
22. Klir, G. J., & Yuan, B., (1995). Fuzzy sets and fuzzy logic, Theory and Applications, third edition. Prentice Hall P T R, New Jersey.
23. Ebadi, M. J., Suleiman, M., Fudziah, B. T. Ismail, Ahmadian, A., & Salahshour, S., (2013). A New Distance Measure for Trapezoidal Fuzzy Numbers. Mathematical Problems in Engineering. Volume 2013, Article ID 424186, 4 pages, http://dx.doi.org/10.1155/2013/424186.
24. Kaufmann, A., & Gupta, M. M., (1985). Introduction to fuzzy arithmetic: Theory and applications, third edition. Van Nostrand Reinhold Company.
25. Kaveh, A., & Talatahari, S., (2010). A novel heuristic optimization method: charged system search. Acta Mechanica , 213(3-4), 267–289.
26. Jin, M., Li, X., & Wang, D., (2013). A new algorithmic scheme for computing characteristic sets. Journal of Symbolic Computation. , 50, 431–449.
27. Muzzioli, S., & Reynaerts, H., (2007). The solution of fuzzy linear systems by non-linear programming: a financial application. European Journal of Operational Research , 177, 1218–1231.
28. Nasrabadi, E., Khorram, E., & Allahviranloo, T., (2012) Fuzzy economic equilibrium problems with fuzzy demand and supply functions. Journal of Intelligent & Fuzzy Systems, 23(5), 267–276.
29. Pal, S. K., & Maji, p., (2004). Fuzzy mathematical morphology. In Fuzzy Techniques in Image Processing, Physica.
30. Precup, R. E., & Hellendoorn, H., (2011). A survey on industrial applications of fuzzy control. Computers in Industry, 62(3), 213–226.
31. Tacu, A., Aluja, J. G., & Teodorescu, H., (1994). Fuzzy systems in economy and engineering, third edition. Hourse of The Romanian Acad.
32. Vroman, A., Deschrijver, G., & Kerre, E. E., (2007). Solving systems of linear fuzzy equations by parametric functions. IEEE Transactions on Fuzzy Systems , 157, 370–384.
33. Vroman, A., Deschrijver, G., & Kerre, E. E., (2007). Solving systems of linear fuzzy equations by parametric functions- an improved algorithm. Fuzzy Sets and Systems , 158, 1515–1534.
34. Wen-Tsun, W. (1984). Basic principles of mechanical theorem proving in geometrics. Journal of Systems Science and Mathematical Sciences. , 4, 207–235.
35. Wen-Tsun, W. (1984). On the decision problem and the mechanization of theorem-proving in elementary geometry. Contemp. Math. , 29, 213–234.
36. Wen-Tsun, W. (2001). Mathematics Mechanization: Mechanical Geometry Theorem-Proving, Mechanical Geometry Problem-Solving and Polynomial Equations-Solving. Mathematics and Its Applications, BeijingbScience Press, London.
37. Wen-Tsun, W., & Gao, X. (2007). Mathematics mechanization and applications after thirty years. Frontiers of Computer Science. China , 1, 1–8.
38. Zadeh, L.A., (1965). Fuzzy sets. Information Control, 8, 338–353.
39. Zadeh, L.A., (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences , 8, 199–249.