A global approach to the design of discrete-time cellular neural networks for associative memories

In this paper a global design method for associative memories using discrete-time cellular neural networks (DTCNNs) is presented. The proposed synthesis technique enables to realize associative memories with several advantageous features. First of all, grey-level as well as bipolar images can be stored. Moreover, the proposed approach generates networks with learning and forgetting capabilities. Finally, it is possible to design networks with any kind of predetermined interconnection structure. In particular, neighbourhoods without line crossings can be chosen, greatly simplifying the VLSI implementation of the designed DTCNNs. In the first part of this work a model of a multilevel threshold network is presented and a stability analysis is carried out using basic notions deriving from non-linear dynamical system theory. The synthesis procedure is then developed by means of a pseudoinversion technique, assuring learning and forgetting capabilities of the designed DTCNN. The use of a neighbourhood without line crossings is also discussed. Simulation results are reported to show the capability of the proposed approach.