Linguistic CMAC for Multi-Attribute Decision Making
Jonathan Lawry · 2009
The multi-attribute decision making problem engages in the propagation of information, which often is highly uncertain or imprecise. Cerebel- lar 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 As- signment is proposed to map the relationship between attributes and a decision variable. We use mass as- signment of attribute variables to calculate the ap- propriateness measure that is equivalent to the prob- ability of the unit in the CMAC selected by the at- tributes. 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 Lin-