Robustness of Fuzzy Flip-Flop based Neural Networks

In this paper the robustness of three different types of Fuzzy Flip-Flop based Neural Network (FNN) and the standard tansig based neural networks is compared from the various test function approximation goodness points of view. It is tested how well the fuzzy flip-flop based and the simulated neural networks handle the test data sets outlier points. The robust design of the FNN is presented, and the best suitable fuzzy neuron type is emphasized. Furthermore, the sensitivity of fuzzy neural networks to the fuzzy neuron type and hidden layers neuron number is evaluated.