Fuzzy min-max neural networks for function approximation
Patrick K. Simpson, Gerhard Jahns · 2002
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.>