An Approach to Knowledge Extraction From ANN Through Formal Concept Analysis - Computational Tool Proposal: SOPHIANN

Due to their capability of dealing with nonlinear problems, artificial neural networks (ANN) are widely used with several purposes. Once trained, they are capable to solve unprecedented situations, keeping tolerable errors in their outputs. However, humans cannot assimilate the knowledge kept by those nets, since such knowledge is implicitly represented by their connections weights. So, in order to facilitate the extraction of rules that describe the knowledge of ANN, formal concept analysis (FCA) and rule extraction algorithms as the next closure algorithm have been used. In this work, this method is implemented on SOPHIANN, a computational tool that combines ANN, FCA and the rule extraction algorithms to compute the minimal implication base (stem base). As an example, solar energy systems are the domain application considered here, due to their importance as substitutes of traditional energy systems