Fuzzy adaptive logic networks

In this study, we elaborate on an important synergy between geometry and fuzzy logic in pattern recognition and show it translates into a coherent architecture of a classifier. The crux of the proposed topology lies in a collection of simple linear classifiers (perceptrons) being combined into a logically coherent topology. In a nutshell: perceptrons come with a simple geometrical interpretation while processing based on fuzzy operators (AND and OR logic units-fuzzy neurons) results in highly transparent and interpretable results. When combined together, forming a fuzzy adaptive logic network they give rise to the computing construct that retains the advantages of these two paradigms of information processing. We discuss a comprehensive development environment of adaptive logic networks and show their application to several classification problems.