Development of a Fuzzy Logic System to Identify the Risk of Projects Financed from Structural Funds

  • Marcel Ioan Boloş University of Oradea
  • Diana-Claudia Sabău-Popa Departament of Finance-Accounting, Faculty of Economic Sciences, University of Oradea Romania, 410087 Oradea, Universitatii St. 1
  • Petru Filip 1. Dimitrie Cantemir Christian University, Romania, 040042 Bucharest, Splaiul Unirii, 176 2. Agora University of Oradea, Romania, 410526 Oradea, Piata Tineretului, 8 3. University of Oradea Romania, 410610 Oradea, University Street, 1
  • Adriana Manolescu

Abstract

The fuzzy logic system developed in this research paper seeks to identify the financial risk of projects financed from structural funds when changes occur in project values, in the duration of the projects and in the implementation durations. Those two factors are known to influence the financial risk. The fuzzy system was simulated using Matlab and the results showed its operation and the conclusion that the financial risk of the project is dependent on the developments values and on the implementation duration. The developed and tested fuzzy logic system provides information on financial risk intensity organized into three categories: small, medium and large and on the inflection point of transition from low risk to high risk. This is considered an early warning system for the management staff with responsibilities in structural funds.

References

[1] A. Altrock, Fuzzy logic and Neuro Fuzzy Logic Applications Explained (1995); Prentice Hall, Englewood Cliffsm.

[2] I.A. Bradea, C. Delcea, R.M. Paun (2014); Managing and Controlling the KRIs in Hospitals Proceedings of 24rd IBIMA Conference: Crafting Global Competitive Economies: 2020 Vision Strategic Planning & Smart Implementation, Italy, ISBN: 978-0-9860419-3-8, 1824- 1830.

[3] I.A. Bradea (2014); Risks in Hospitals. Assessment and Management The Romanian Economic Journal, ISSN: 2286-2056, 54(XVII): 25-37.

[4] R. Fuller (2000); Introduction to Neurofuzzy Systems, Advances an Inteligent and Soft Computing, vol 2, ISBN 978-3-7908-1256-5.

[5] C. Kahraman, I. Kaya (2010); A Fuzzy Multicriteria Methodology for selection Among Energy Alternatives, IExpert Systems with Applications, 37(9): 6270-6281.

[6] S.M. Mousave, F. Joloi, R. Tavakkoli-Moghaddam (2013); A Fuzzy Stochastic Multi- Attribute Group Decision-Making Approach for Selection Problems, Group Decision and Negotiation, 22(2): 207-233.

A Fuzzy Stochastic Multi- Attribute Group Decision-Making Approach for Selection Problems, Group Decision and Negotiation, 22(2): 207-233.
http://dx.doi.org/10.1007/s10726-011-9259-1

[7] S. Nadaban (2015); Fuzzy Euclidean Normed Spaces for Data Mining Applications, International Journal of Computers Communications & Control, 10(1): 70-77.
http://dx.doi.org/10.15837/ijccc.2015.1.1564

[8] S. Nadaban, I. Dzitac (2014); Atomic Decompositions of Fuzzy Normed Linear Spaces for Wavelet Applications, Informatica, http://dx.doi.org/10.15388/Informatica.2014.33, 25(4): 643-662.
http://dx.doi.org/10.15388/Informatica.2014.33

[9] A. Nieto-Morote, F. Ruz-Vila (2011); A Fuzzy Approach to Construction project Risk Assessement, International Journal of Project Management, 29(2): 220-231.

A Fuzzy Approach to Construction project Risk Assessement, International Journal of Project Management, 29(2): 220-231.
http://dx.doi.org/10.1016/j.ijproman.2010.02.002

[10] E. Scarlat, N. Chiriţă, I.A. Bradea (2012); Indicators and Metrics Used in the Enterprise Risk Management (ERM), Economic Computation and Economic Cybernetics Studies and Research Journal, 4(46):5-18.

[11] M.L. Tseng (2010); Implementation and Performance Evaluation Using the Fuzzy network Balanced Scorecard, Computers & Education, 55(1): 188-201.
http://dx.doi.org/10.1016/j.compedu.2010.01.004

[12] Y.L. Xu, J.F.Y. Yeung, A.P.C. Chan, D.W.N. Chan, S.Q. Wang, Y.L. Ke (2010); Developing a Risk Assessement Model for PPP project in China - A Fuzzy Synthetic Evaluation, Automation in Construction, 19(7): 9293-943.

Developing a Risk Assessement Model for PPP project in China - A Fuzzy Synthetic Evaluation, Automation in Construction, 19(7): 9293-943.

[13] L.A. Zadeh (1992); Fuzzy logic and the calculus of fuzzy if-then rules, Proceedings of the 22nd Intl. Symp. on Multiple-Valued Logic, Los Alamitos, CA: IEEE Computer Society Press, 530-561.
http://dx.doi.org/10.1109/ismvl.1992.186834

[14] L.A. Zadeh (1996); Fuzzy logic - computing with words, IEEE Trans. Fuzzy Systems, 4(2):103-111.
http://dx.doi.org/10.1109/91.493904
Published
2015-06-22
How to Cite
BOLOŞ, Marcel Ioan et al. Development of a Fuzzy Logic System to Identify the Risk of Projects Financed from Structural Funds. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 4, p. 480-491, june 2015. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1914>. Date accessed: 05 july 2020. doi: https://doi.org/10.15837/ijccc.2015.4.1914.

Keywords

Fuzzy Logic System (FLS), artificial intelligence, financial risk, structural funds, centroid method