Fuzzy Associative Conjuncted Maps Network
Résumé
The fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that associates data of nonlinearly related inputs and outputs. In the network, each input or output dimension is represented by a feature map that is partitioned into fuzzy or crisp sets. These fuzzy sets are then conjuncted to form antecedents and consequences, which are subsequently associated to form IF–THEN rules. The associative memory is encoded through an offline batch mode learning process consisting of three consecutive phases. The initial unsupervised membership function initialization phase takes inspiration from the organization of sensory maps in our brains by allocating membership functions based on uniform information density. Next, supervised Hebbian learning encodes synaptic weights between input and output nodes. Finally, a supervised error reduction phase fine-tunes the network, which allows for the discovery of the varying levels of influence of each input dimension across an output feature space in the encoded memory. In the series of experiments, we show that each phase in the learning process contributes significantly to the final accuracy of prediction. Further experiments using both toy problems and real-world data demonstrate significant superiority in terms of accuracy of nonlinear estimation when benchmarked against other prominent architectures and exhibit the network's suitability to perform analysis and prediction on real-world applications, such as traffic density prediction as shown in this paper.
Mots clés
unsupervised learning
Fuzzy associative conjuncted maps (FASCOM)
fuzzy associative memory
fuzzy neural networks
Hebbian learning
Iris plant classification
multivariate data analysis
Nakanishi's nonlinear estimation tasks
neurofuzzy systems
supervised learning
traffic density prediction
two-spiral classification
unsupervised learning.
Domaines
Traitement des images [eess.IV]
Origine : Fichiers produits par l'(les) auteur(s)
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