dFasArt: Dynamic neural processing in FasArt model

The temporal character of the input is, generally, not taken into account in the neural models. This paper presents an extension of the FasArt model focused on the treatment of temporal signals. FasArt model is proposed as an integration of the characteristic elements of the Fuzzy System Theory in an ART architecture. A duality between the activation concept and membership function is established. FasArt maintains the structure of the Fuzzy ARTMAP architecture, implying a static character since the dynamic response of the input is not considered. The proposed novel model, dynamic FasArt (dFasArt), uses dynamic equations for the processing stages of FasArt: activation, matching and learning. The new formulation of dFasArt includes time as another characteristic of the input. This allows the activation of the units to have a history-dependent character instead of being only a function of the last input value. Therefore, dFasArt model is robust to spurious values and noisy inputs. As experimental work, some cases have been used to check the robustness of dFasArt. A possible application has been proposed for the detection of variations in the system dynamics.