Chapter 1 A Linguistic CMAC vs. a Linguistic Decision Tree for Decision Making

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 at- tributes and the goal variable that is often highly nonlinear. Linguistic Decision Trees based on label semantics have been used as a decision maker in many areas. A linguistic decision tree presents information propagation from input attributes to a goal variable based on transparent linguistic rules. 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.

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