A neurofuzzy algorithm for learning from complex granules

Abstract

In the context of complex granule computations within the Interactive Granular Computating (IGC) paradigm we frame a cognitive task where user perceptions of the suitability of a good are in relation to the parameters of the device producing it, all within a learning loop aimed at continuously improving those perceptions. We achieve this goal by extending the Fuzzy Inference System (FIS) paradigm to contexts where variables reckoning the user perceptions live in a non-metric space, hence neither users nor the learning algorithm have access to their true value. Namely, receiving in input a set of both crisp and fuzzy variables (respectively, from the hard_suit and the soft_suit of the c-granule to account for user and device logs), the inference system is asked to compute via the link_suit a set of crisp parameters satisfying some fuzzy evaluations stated by the user. A further complication is that the outputs are evaluated exactly in terms of the true unknown values held by the fuzzy attributes, which in turn must be inferred by the system. The whole work arose from everyday life problems faced by the European Project Social&Smart with the aim of optimally regulating household appliance runs. It represents a special instance of Interactive Rough Granular Computing (IRGC) that we face with a two-phase procedure that is reminiscent of the distal learning in neurocontrol. A web service is available where the reader may check the efficiency of the assessed procedure.