Convergent activation dynamics in continuous time networks
Neural Networks
A comparison of five algorithms for the training of CMAC memories for learning control systems
Automatica (Journal of IFAC)
Machine Learning
A framework for linguistic modelling
Artificial Intelligence
Decision tree learning with fuzzy labels
Information Sciences—Informatics and Computer Science: An International Journal
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
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Linguistic Decision Trees based on label semantics have been used as a classifier or predictor in many areas. A linguistic decision tree presents information propagation from input attributes to a goal variable based on transparent linguistic rules. The relationship between input attributes and the goal variable is often highly nonlinear. Cerebellar Model Articulation Controller (CMAC) belongs to the family of feed-forward networks with a single linear trainable layer. A CMAC has the feature of fast learning, and is suitable for modeling any non-linear relationship. Combining label semantics and an original CMAC, a linguistic CMAC based on Mass Assignment on labels is proposed to map the relationship between the attributes and the goal variable. The proposed LCMAC model is functionally equivalent to a linguistic decision tree, and takes the advantage of fast local training of the original CMAC and the advantage of transparency of a linguistic decision tree.