[1. Georgieva, P. V. FSSAM: A Fuzzy Rule-Based System for Financial Decision Making in Real Time. Handbook of Fuzzy Sets Comparison – Theory, Algorithms and Applications. Science Gate Publishing, 2016, pp. 121-148.10.15579/gcsr.vol6.ch6]Search in Google Scholar
[2. Herrera, F., M. Lozano, E. Herrera-Viedma, J. Verdegay. Fuzzy Tools to Improve Genetic Algorithms. – In: Proc. of European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, 1994, pp. 1532-1539.]Search in Google Scholar
[3. Peneva, V., I. Popchev. Fuzzy Logic Operators in Decision-Making. – International Journal Cybernetics and Systems, Robert Trappl, Ed., Vol. 30, 1999, No 6, pp. 725-745.10.1080/019697299124966]Search in Google Scholar
[4. Georgieva, P. V., I. Popchev, S. Stoyanov. A Multi-Step Procedure for Asset Allocation in Case of Limited Resources. – Cybernetics and Information Technologies, Vol. 15, 2015, No 3, pp. 41-51.10.1515/cait-2015-0040]Search in Google Scholar
[5. Peneva, V., I. Popchev. Multicriteria Decision Making Based on Fuzzy Relations. – Cybernetics and Information Technologies, Vol. 8, 2008, No 4, pp. 3-12.]Search in Google Scholar
[6. Georgieva, P. V. Applying FSSAM for Currency Rates Forecasting. – In: Transactions on Machine Learning and Artificial Intelligence, Manchester, SSE UK, Vol. 4, 2016, No 3, pp. 30-40.10.14738/tmlai.43.2079]Search in Google Scholar
[7. Georgieva, P. V. Fuzzy Rule-Based Systems for Decision-Making. – Engineering Sciences, BAS, Vol. LIII, 2016, No 1, pp. 5-16.]Search in Google Scholar
[8. Mavrov, D., I. Radeva, K. Atanassov, L. Doukovska, I. Kalaykov. InterCriteria Software Design: Graphic Interpretation within the Intuitionistic Fuzzy Triangle. – In: International Symposium on Business Modeling and Software Design (BMSD’15), Milano, 2015, pp. 279-283.]Search in Google Scholar
[9. Peneva, V., I. Popchev. Fuzzy Multi-Criteria Decision Making Algorithms. – Compt. Rend. Acad. bulg. Sci., Vol. 63, 2010, No 7, pp. 979-991.]Search in Google Scholar
[10. Zafari, A. Developing a Fuzzy Inference System by Using Genetic Algorithm and Expert Knowledge. Netherlands, Enschede, 2014.]Search in Google Scholar
[11. Zadeh, L. A Theory of Approximate Reasoning. – Machine Intelligence, Vol. 9, 1979, pp. 149-194.]Search in Google Scholar
[12. Popchev, I., P. Georgieva. A Fuzzy Approach for Solving Multicriteria Investment Problems. – In: Innovative Techniques in Instruction Technology, e-Learning, e-Assessment, and Education. M. Iskander, Ed. Springer Science+Business Media B. V., 2008, pp. 427-431.10.1007/978-1-4020-8739-4_75]Search in Google Scholar
[13. Sugeno, M. Industrial Applications of Fuzzy Control. Japan, Elsevier Science Pub, Co., 1985.]Search in Google Scholar
[14. Melin, P., O. Castillo, E. Ramírez. Analysis and Design of Intelligent Systems Using Soft Computing Techniques. – Series: Advances in Soft Computing, Vol. 41, 2007.10.1007/978-3-540-72432-2]Search in Google Scholar
[15. Jang, R. Fuzzy Inference Systems. NJ, Prentice-Hall, 1997.]Search in Google Scholar
[16. Goldberg, D., K. Deb. A Comparative Analysis of Selection Schemes Used in Genetic Algorithms. – In: Foundations of Genetic Algorithms, Los Altos, Morgan Kaufmann, 1991, pp. 69-93.10.1016/B978-0-08-050684-5.50008-2]Search in Google Scholar
[17. Popchev, I., V. Peneva. An Algorithm for Comparison of Fuzzy Sets. – Fuzzy Sets and Systems, Elsevier Science Publishers, Norht-Holland, Amsterdam, Vol. 60, 1993, No 1, pp. 59-65.10.1016/0165-0114(93)90289-T]Search in Google Scholar
[18. Radeva, I. Multicriteria Fuzzy Sets Application in Economic Clustering Problems. – Cybernetics and Information Technologies, Vol. 17, 2017, No 3, pp. 29-46.10.1515/cait-2017-0028]Search in Google Scholar
[19. Radeva, I. Multi-Criteria Models for Cluster Design. – Cybernetics and Information Technologies, Vol. 13, 2013, No 1, pp. 18-33.10.2478/cait-2013-0003]Search in Google Scholar