Comparison of a Neural Network Based on Fuzzy Flip-Flops and an MLP Robustness in Function Approximation Robustness of Fuzzy Flip-Flop Based Neural Networks
Acta Technica Jaurinensis, Rita Lovassy, László Tamás Kóczy, László Gál · 2011
In this paper two types of neural networks, namely the traditional tansig based neural networks and the multilayer perceptrons based on fuzzy flip- flops (F 3 NN) trained by the Bacterial Memetic Algorithm with Modified Operator Execution Order (BMAM) are tested and compared on their robustness to test functions outliers. The robust design of the F 3 NN is presented, and the best suitable fuzzy neuron type is emphasized. As our major motivation in these investigations was to construct a technology for the creation of real hardware MLPs and for this reason the fuzzy flip-flop based F 3 NNs obviously offered much simpler and cheaper possibility for hardware implementation compared to a relatively complicated tansig type neural network.