Fuzzy min-max neural networks for function approximation

Abstract

The fuzzy min-max function approximation neural network is introduced, and results of its performance on a sample problem are presented. The function approximation network is realized by modifying the previously developed fuzzy min-max clustering network to include an output layer that sums and thresholds the hidden layer membership functions. The approximation of a test function to a small tolerance and robustness when trained on sparse data is demonstrated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>