Improvement to the cooperative rules methodology by using the ant colony system algorithm
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2099/3613
Tipus de documentArticle
Data publicació2001
EditorUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
The cooperative rules (COR) methodology [2] is based on a combinatorial search of cooperative rules performed over a set of previously generated candidate rule
consequents. It obtains accurate models preserving the highest interpretability of the linguistic fuzzy rule-based systems.
Once the good behavior of the COR methodology has been proven in previous works, this contribution focuses on developing the process with a novel kind of metaheuristic algorithm: the ant colony system one. Thanks to the capability of this algorithm to include heuristic information, the learning process is accelerated without model accuracy losses.
Its behavior is successful compared with other processes based on genetic algorithms and simulated annealing when solving two modeling applications.
ISSN1134-5632
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
9-casillas.pdf | 308,6Kb | Visualitza/Obre |