A cascade of linguistic CMAC neural networks for decision making

A CMAC (Cerebellar Model Articulation Controller) is a kind of feed-forward neural networks (FFNNs), but the feature of fast learning makes it different from classic FFNNs. A CMAC has a single linear trainable layer, but due to the input information is distributed in a hypercube grid, it is suitable for modeling any non-linear relationship. It has been proved that a linguistic CMAC (LCMAC) based on the Label Semantics can represent the rules that a linguistic decision tree (LDT) does when they are used to map the relationship between inputs and outputs. In order to overcome the `curse of dimensionality´ of an LCMAC in memory use for multi-attribute decision making, a cascade of LCMACs is proposed, and the linguistic interpretation of a cascade of LCMACs is investigated to break the convention of neural networks as a black box. An algorithm for training a cascade of LCMACs is developed. The proposed cascade of LCMACs has a great reduction in the use of memory units, and as a decision maker, it can achieve better performance in accuracy and F₁-score than the cascade of LDTs does for those data sets with non-linear relations between inputs and outputs.