Optimal implementation of neural activation functions in programmable logic using fuzzy logic

This work presents a methodology for implementing neural activation function in programmable logic using tools from fuzzy logic. This methodology will allow implementing these intrinsic non-linear functions using comparators and simple linear modellers, easily implemented in programmable logic. This work is particularized to the case of a hyperbolic tangent, the most common function in neural models, showing the excellent results yielded with the proposed approximation.