Interpretability-accuracy improvement in a neuro-fuzzy ART based model of a DC motor

Abstract The aim of this paper is to propose a general methodology applicable to any rule based fuzzy model generated by any precise or linguistic fuzzy algorithm to improve the linguistic-accuracy trade-off. Here, the neuro-fuzzy system FasArt (Fuzzy Adaptive System ART based) is used for its proven model capabilities, as shown in previous papers and works. If does, however, have the usual drawbacks, from the linguistic point of view, of most fuzzy modeling methods found in the scientific literature. A fuzzy model of a DC motor is generated by FasArt, whose performance is a good estimation of the motor's behavior, then this performance is improved by a better interpretability of the knowledge attained and stored by this fuzzy model. The main idea behind this approach is to find a fuzzy model with enough accuracy and an adequate capacity of explanation or interpretability of its data acquired knowledge. The modeling process can thus be seen as knowledge extraction in human or linguistic terms: from a numeric level (data) to a symbolic one (linguistic fuzzy rules).