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Using the NextClosure algorithm to extract rules from trained neural networks application in solar energy systems

Due to their capability of dealing with nonlinear problems, artificial neural networks (ANN) is 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 the NextClosure algorithm have been used. Such method is presented in this work, combining ANN, FCA and the NextClosure algorithm 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.