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http://hdl.handle.net/1942/25657
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DC Field | Value | Language |
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dc.contributor.author | AL_GHZAWI, Ahmad | - |
dc.contributor.author | SAMMOUR, George | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2018-03-06T10:13:40Z | - |
dc.date.available | 2018-03-06T10:13:40Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Time conference (TIME-2017), Amman, Jordan, 21/05/2017 | - |
dc.identifier.uri | http://hdl.handle.net/1942/25657 | - |
dc.description.abstract | Fuzzy Cognitive Maps are emerging as an important new tool in economic modeling. This study investigates the use of fuzzy cognitive maps with their learning algorithms, based on genetic algorithms, for the purposes of economic prediction. The case study data are extracted from the Jordanian social sociality revenues and expanse for the last 120 months; The Real-Code genetic algorithm and structure optimization algorithm were chosen for their ability to select the most significant relationships between the concepts and to predict future development of the Jordanian social sociality revenues and expenses. Furthermore, fuzzy cognitive maps are able to calculate prediction errors accurately. The study shows that fuzzy cognitive maps models clearly predict the future of a complex financial system with incoming and outgoing flows. Consequently, this research confirms the benefits of fuzzy cognitive maps applications as a tool for scholarly researchers, economists and policy makers. | - |
dc.language.iso | en | - |
dc.subject.other | fuzzy cognitive maps; prediction problems; modeling; learning algorithms; Jordanian social sociality | - |
dc.title | Using Fuzzy Cognitive Maps to predict the economic sustainability of Jordan Social Security | - |
dc.type | Conference Material | - |
local.bibliographicCitation.conferencedate | 21/05/2017 | - |
local.bibliographicCitation.conferencename | Time conference (TIME-2017) | - |
local.bibliographicCitation.conferenceplace | Amman, Jordan | - |
local.format.pages | 12 | - |
local.bibliographicCitation.jcat | C2 | - |
dc.relation.references | 1. Kosko, B., Fuzzy cognitive maps. International Journal of Man-Machine Studies, 1986. 24(1): p. 65-75. 2. Groumpos, P.P. and C.D. Stylios, Modelling supervisory control systems using fuzzy cognitive maps. Chaos, Solitons & Fractals, 2000. 11(1–3): p. 329-336. 3. Nápoles, G., et al., On the convergence of sigmoid Fuzzy Cognitive Maps. Information Sciences, 2016. 349–350: p. 154-171. 4. Papakostas, G.A., et al., Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems. Expert Systems with Applications, 2012. 39(12): p. 10620-10629. 5. Salmeron, J.L., Fuzzy cognitive maps for artificial emotions forecasting. Applied Soft Computing, 2012. 12(12): p. 3704-3710. 6. Lu, W., et al., The modeling of time series based on fuzzy information granules. Expert Systems with Applications, 2014. 41(8): p. 3799-3808. 7. Homenda, W., A. Jastrzebska, and W. Pedrycz, Time Series Modeling with Fuzzy Cognitive Maps: Simplification Strategies, in Computer Information Systems and Industrial Management: 13th IFIP TC8 International Conference, CISIM 2014, Ho Chi Minh City, Vietnam, November 5-7, 2014. Proceedings, K. Saeed and V. Snášel, Editors. 2014, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 409-420. 8. Ketipi, M.K., et al., A flexible nonlinear approach to represent cause–effect relationships in FCMs. Applied Soft Computing, 2012. 12(12): p. 3757-3770. 9. Nápoles, G., et al., Learning and Convergence of Fuzzy Cognitive Maps Used in Pattern Recognition. Neural Processing Letters, 2016: p. 1-14. 10. Bueno, S. and J.L. Salmeron, Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications, 2009. 36(3, Part 1): p. 5221-5229. 11. Stach, W., et al., Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 2005. 153(3): p. 371-401. 12. Homenda, W., A. Jastrzebska, and W. Pedrycz. Modeling time series with fuzzy cognitive maps. in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2014. 13. Pocz, K., et al. Learning fuzzy cognitive maps using Structure Optimization Genetic Algorithm. in Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on. 2015. 14. Herrera, F., M. Lozano, and J.L. Verdegay, Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. Artificial Intelligence Review, 1998. 12(4): p. 265-319. | - |
local.type.refereed | Non-Refereed | - |
local.type.specified | Paper | - |
item.fulltext | With Fulltext | - |
item.fullcitation | AL_GHZAWI, Ahmad; SAMMOUR, George & VANHOOF, Koen (2017) Using Fuzzy Cognitive Maps to predict the economic sustainability of Jordan Social Security. In: Time conference (TIME-2017), Amman, Jordan, 21/05/2017. | - |
item.accessRights | Open Access | - |
item.contributor | AL_GHZAWI, Ahmad | - |
item.contributor | SAMMOUR, George | - |
item.contributor | VANHOOF, Koen | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Al Ghzawi.pdf | Conference material | 737.87 kB | Adobe PDF | View/Open |
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