A cascade of linguistic CMAC neural networks for decision making

Abstract

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.