Approximation by Max-Product Neural Network Operators of Kantorovich Type

In the present paper, we develop the theory of max-product neural network operators in a Kantorovich-type version, which is suitable in order to study the case of L^p-approximation for not necessarily continuous data. Moreover, also the case of the pointwise and uniform approximation of continuous functions is considered. Finally, several examples of sigmoidal functions for which the above theory can be applied are presented.