Fuzzy Granulation-Based Cascade Fuzzy Neural Networks Optimized by GA-RSL

This paper is concerned with cascade fuzzy neural networks and its optimization. These networks come with sound and transparent logic characteristics by being developed with the aid of AND and OR fuzzy neurons and subsequently logic processors (LPs). We discuss main functional properties of the model and relate them to its form of cascade type of systems formed as a stack of LPs. The structure of the network that deals with a selection of a subset of input variables and their distribution across the individual LPs is optimized with the use of genetic algorithms (GA). We discuss random signal-based learning (RSL), a local search technique, aimed at further refinement of the connections of the neurons (GA-RSL). We elaborate on the interpretation aspects of the network and show how this leads to a Boolean or multi-valued logic description of the experimental data. Two kinds of standard data sets are discussed with respect to the performance of the constructed networks and their interpretability.