Hardware oriented semistate descriptions of functional artificial neural networks

Three hardware oriented semistate descriptions for the functional artificial neural network (FANN) are introduced to pave the way for VLSI realization. The first one is current-mode based in order to use current mirrors, current multipliers and integrators/differentiators. Next we show a voltage-current mixed-mode one realizable with OTAs, and finally one with all voltage variables realizable through differential operational amplifiers, etc. The functional artificial neural network under consideration uses neurons which are functionals. The Fock space in which these neurons are represented by Volterra functionals is a reproducing kernel Hilbert space, with synaptic weights as functions themselves as introduced by de Figueiredo and his students. This functional neural network can capture the dynamics present in real-world (continuous-time-parameter) nonlinear systems, enabling it to model them, as well as simulate their behavior in a computer-based environment.