A Hopfield neural network approach to decentralized self-synchronizing sensor networks

Decentralized inference of a sensor network in the difficult case of a nonreciprocal nonlinear context is investigated by transforming the sensor network into a Hopfield neural network. Equilibrium states of the latter correspond to situations of global consensus in the sensor network, characterized by suitable regions (consensus regions) in the space of its parameters. The said transformation was recently proposed by the author and applied to the simple case of three sensors. The general case of more than three sensors is investigated in the present paper. A procedure is developed for determining the structure and the properties of the consensus regions.