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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]

This work develops and tests a neighborhood-based approach to the Gauss-Newton Bayesian regularization trainingmethod for feedforward backpropagation networks. The proposed method improves the training efficiency, significantly reducing requirements on memory and computational time while maintaining the good generalization feature of the original algorithm. This version of the Gauss-Newton Bayesian regularization greatly expands the scope of application of the original method, as it allows training networks up to 100 times larger without losing performance. [ABSTRACT FROM AUTHOR]

A neuro-fuzzy system for isolated hand-written digit recognition using a similarity fuzzy measure is presented. The system is composed of two main blocks: a first block that normalises the input and compares it with a set of fuzzy patterns, and a second block with a multilayer perceptron (MLP) to perform the definitive classification. The comparison with the fuzzy patterns is carried out via a fuzzy similarity measure that uses the Yager parametric norms and co-norms. Along this work, several values of the parameter have been studied, in order to obtain the optimum. The simplicity of the method makes it extremely quick. Recognition accuracy of the method is about 90% single classification, and close to 97,5% classification scheme. [ABSTRACT FROM AUTHOR]