Active Hebbian learning algorithm to train fuzzy cognitive maps

By Elpiniki I. Papageorgiou, Chrysostomos D. Stylios, P. Groumpos. International Journal of Approximate Reasoning, 2004

370 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f502b38cec3817006269f94d4ac42d727a813818

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Elpiniki I. Papageorgiou
2. Lukasz Kurgan
3. Witold Pedrycz
4. Marek Z. Reformat
5. Wojciech Stach
6. Kasper Kok
7. Hongbin Liu
8. Rosa M. Rodríguez

Citations in the corpus

• Learning Algorithms for Fuzzy Cognitive Maps—A Review Study (2012, methodology): “introduced an active HL (AHL) algorithm, which modifies all the weights at each iteration step by using initial human intervention and requiring the determination of a sequence of activation concepts by keeping the main principles of Hebbian rule [38].”
• Genetic learning of fuzzy cognitive maps (2005, methodology): “Hebbian learning, and genetic algorithms, but so far none of proposed methods can be adopted as a formal methodology that is suitable for FCMs convergence [30].”