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Fuzzy ART and Fuzzy ARTMAP models arise from the synergy between the Fuzzy Set Theory and the Adaptive Resonance paradigm (ART). In this work, the performance of these models and the use of Fuzzy ARTMAP for function approximation are studied. In a first analysis, a relationship between the model parameters and the features of the generated categories is established. In the second part, the connection between these categories and the capacity of prediction of the model is analytically described. Joining these two studies, the link between the parameters and the prediction error of the model is found, in the form of bounds for the prediction error depending on the model parameters and the characteristics of the data used in the learning. These results provide a quantitative description of the parameter influence on the architecture behavior, opening the use of Fuzzy ARTMAP as a model for the unknown dynamic system identification from input/output data. To illustrate the theoretical developments, several experiments have been carried out using different kinds of functions, which show the accuracy of the proposed bounds. [ABSTRACT FROM AUTHOR]

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. [Copyright &y& Elsevier]