An inverse Hollis-Paulos artificial neural network

The Hollis-Paulos artificial neural network (HPANN) is convenient in terms of its possibility for realization of variable weight artificial neural networks (ANN's) in very large scale integration (VLSI) by MOS transistor circuits, though it is nondynamical and not driven by external inputs. Here we introduce dynamics and inputs into the HPANN and show that over the range of operation covered by the Hollis-Paulos theory the system has an inverse. In particular, we derive that inverse, in semistate form, and give simulation results on its operation, showing how well the input to the original HPANN can be recovered from the output of the HPANN when fed into the inverse system. A comparison is made with the previous inverse of the Hopfield ANN. Possible applications of these inverse systems are to decoding of transmitted ANN signals and to inverse filtering for the extraction of input signals from processed signals.