Functional Equivalence between S-neural Networks and Fuzzy Models
Claudio Moraga, Karl-Heinz Temme · Studies in fuzziness and soft computing · 2002
A family of S-functions is introduced and characterized. S-functions may be used as activation functions in neural networks and allow the interpretation of the activity of the artificial neurons as fuzzy if-then rules, where the degree of satisfaction of the premises for a given input is calculated by means of the symmetric summation. These rules are appropriate to model compensating systems.