Extracting fuzzy rules from "mental" images generated by a modified WISARD perceptron

The pioneering WiSARD weightless neural classifier is based on the collective response of RAM-based neurons. The ability of producing prototypes, analog to “mental images”, from learned categories, was first introduced in the DRASiW model. By counting the frequency of write accesses at each RAM neuron during the training phase, it is possible to associate the most accessed addresses to the corresponding input field contents that defined them. This work is about extracting information from such frequency counting in the form of fuzzy rules as an alternative way to describe the same mental images produced by DRASiW as logical prototypes.