Systematic Review of Learning Algorithms for Large-Scale Fuzzy Cognitive Maps
DOI:
https://doi.org/10.70882/josrar.2025.v2i5.188Keywords:
Fuzzy Cognitive Maps, Large-Scale Maps, Learning AlgorithmsAbstract
Fuzzy Cognitive Maps (FCMs) are an important and versatile tool for modeling systems with complex dynamics in various domains like medicine, engineering, environmental monitoring, financial systems, among others to aid in decision-making. These domains usually datasets with large number of nodes and connections leading to large-scale maps. This paper reviews learning algorithms for FCMs under Hebbian, Population-based and hybrid classifications. While Hebbian-based algorithms suffer from local optima and generalization issues, population-based algorithms are generally computationally prohibitive from large-scale exploration, which also affects the global search component of hybrid algorithms. A number of algorithms have been developed specifically for large-scale FCMs by adopting problem decomposition, parallelization, sparsity inducement, and multi-agent-based techniques. Considering the great potential of FCMs as a modeling tool in domains with large, complex systems, research on algorithms tailored to large-scale systems remain limited and needs to be explored further.
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