  This paper proposes a novel and insightful activation method termed FPLUS,
which exploits mathematical power function with polar signs in form. It is
enlightened by common inverse operation while endowed with an intuitive meaning
of bionics. The formulation is derived theoretically under conditions of some
prior knowledge and anticipative properties, and then its feasibility is
verified through a series of experiments using typical benchmark datasets,
whose results indicate our approach owns superior competitiveness among
numerous activation functions, as well as compatible stability across many CNN
architectures. Furthermore, we extend the function presented to a more
generalized type called PFPLUS with two parameters that can be fixed or
learnable, so as to augment its expressive capacity, and outcomes of identical
tests validate this improvement.
