Development of application-specific adjacency models using fuzzy cognitive map
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause–effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, kno...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2014
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/37061/ http://psasir.upm.edu.my/id/eprint/37061/1/Development%20of%20application-specific%20adjacency%20models%20using%20fuzzy%20cognitive%20map.pdf |
| _version_ | 1848848506918273024 |
|---|---|
| author | Motlagh, Omid Reza Esmaeili Tang, Sai Hong Homayouni, Sayed Mahdi Grozev, George Papageorgiou, Elpiniki I. |
| author_facet | Motlagh, Omid Reza Esmaeili Tang, Sai Hong Homayouni, Sayed Mahdi Grozev, George Papageorgiou, Elpiniki I. |
| author_sort | Motlagh, Omid Reza Esmaeili |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause–effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology. |
| first_indexed | 2025-11-15T09:35:35Z |
| format | Article |
| id | upm-37061 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:35:35Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-370612015-12-01T07:24:56Z http://psasir.upm.edu.my/id/eprint/37061/ Development of application-specific adjacency models using fuzzy cognitive map Motlagh, Omid Reza Esmaeili Tang, Sai Hong Homayouni, Sayed Mahdi Grozev, George Papageorgiou, Elpiniki I. Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause–effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology. Elsevier 2014-11 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/37061/1/Development%20of%20application-specific%20adjacency%20models%20using%20fuzzy%20cognitive%20map.pdf Motlagh, Omid Reza Esmaeili and Tang, Sai Hong and Homayouni, Sayed Mahdi and Grozev, George and Papageorgiou, Elpiniki I. (2014) Development of application-specific adjacency models using fuzzy cognitive map. Journal of Computational and Applied Mathematics, 270. pp. 178-187. ISSN 0377-0427; ESSN: 1879-1778 http://www.sciencedirect.com/science/article/pii/S037704271400079X 10.1016/j.cam.2014.02.003 |
| spellingShingle | Motlagh, Omid Reza Esmaeili Tang, Sai Hong Homayouni, Sayed Mahdi Grozev, George Papageorgiou, Elpiniki I. Development of application-specific adjacency models using fuzzy cognitive map |
| title | Development of application-specific adjacency models using fuzzy cognitive map |
| title_full | Development of application-specific adjacency models using fuzzy cognitive map |
| title_fullStr | Development of application-specific adjacency models using fuzzy cognitive map |
| title_full_unstemmed | Development of application-specific adjacency models using fuzzy cognitive map |
| title_short | Development of application-specific adjacency models using fuzzy cognitive map |
| title_sort | development of application-specific adjacency models using fuzzy cognitive map |
| url | http://psasir.upm.edu.my/id/eprint/37061/ http://psasir.upm.edu.my/id/eprint/37061/ http://psasir.upm.edu.my/id/eprint/37061/ http://psasir.upm.edu.my/id/eprint/37061/1/Development%20of%20application-specific%20adjacency%20models%20using%20fuzzy%20cognitive%20map.pdf |