In this paper we show two new learning algorithms for a fuzzy min–max neural network. The top down fuzzy min–max (TDFMM) algorithm modifies the classic Simpson's learning algorithm overcoming its main difficulties: the dependence on the presentation order of the patterns and the poor resolutive adaptation to the characteristics of input space. The top down fuzzy min–max regressor (TDFMMR) algorithm extends our neural network to solve regression problems by using a hybrid fuzzy classifier and a gradient descent algorithm.
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