Linguistic CMAC for Multi-Attribute Decision Making

The multi-attribute decision making problem engages in the propagation of information, which often is highly uncertain or imprecise. Cerebellar Model Articulation Controller (CMAC) belongs to the family of feed-forward networks with a single linear trainable layer. CMAC has the feature of fast learning, and is suitable for modeling any non-linear relationship. Combining fuzzy linguistic semantics and CMAC, a linguistic CMAC based on Mass Assignment is proposed to map the relationship between attributes and a decision variable. We use mass assignment of attribute variables to calculate the appropriateness measure that is equivalent to the probability of the unit in the CMAC selected by the attributes. The state of decision variable is decided by the sum of weighted active units in CMAC. We then investigate the equivalence between the black box of the Linguistic CMAC and the transparent box of Linguistic Decision Tree.