An Inverse Hollis-Paulos Artificial
Neural Network
Louiza Sellami and Robert W. Newcomb
Abstract
The Hollis-Paulos artificial neural network (HPANN) is convenient in terms of
its possibility for realization of variable weight ANNs in VLSI by MOS
transistor circuits, though it is non-dynamical 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 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.