A low-complexity fuzzy activation function for artificial neural networks

Abstract—A novel fuzzy-based activation function for artificial neural networks is proposed. This approach provides easy hardware implemen-tation and straightforward interpretability in the basis of IF–THEN rules. Backpropagation learning with the new activation function also has low computational complexity. Several application examples (XOR gate, chaotic time-series prediction, channel equalization, and independent component analysis) support the potential of the proposed scheme. Index Terms—Activation function, fuzzy logic, rule extraction. I