The paper introduces a class of memristor neural networks (NNs) that are characterized by the following salient features. (a) The processing of signals takes place in the fluxâcharge domain and is based on the time evolution of memristor charges. The processing result is given by the constant asymptotic values of charges that are stored in the memristors acting as non-volatile memories in steady state. (b) The dynamic equations describing the memristor NNs in the fluxâcharge domain are analogous to those describing, in the traditional voltageâcurrent domain, the dynamics of a standard (S) cellular (C) NN, and are implemented by using a realistic model of memristors as that proposed by HP. This analogy makes it possible to use the bulk of results in the SCNN literature for designing memristor NNs to solve processing tasks in real time. Convergence of memristor NNs in the presence of multiple asymptotically stable equilibrium points is addressed and some applications to image processing tasks are presented to illustrate the real-time processing capabilities. Computing in the fluxâcharge domain is shown to have significant advantages with respect to computing in the voltageâcurrent domain. One advantage is that, when a steady state is reached, currents, voltages and hence power in a memristor NN vanish, whereas memristors keep in memory the processing result. This is basically different from SCNNs for which currents, voltages and power do not vanish at a steady state, and batteries are needed to keep in memory the processing result.
Di Marco, M., Forti, M., & Pancioni, L. (2017). Memristor standard cellular neural networks computing in the flux-charge domain. NEURAL NETWORKS, 93, 152-164.
|Titolo:||Memristor standard cellular neural networks computing in the flux-charge domain|
|Citazione:||Di Marco, M., Forti, M., & Pancioni, L. (2017). Memristor standard cellular neural networks computing in the flux-charge domain. NEURAL NETWORKS, 93, 152-164.|
|Appare nelle tipologie:||1.1 Articolo in rivista|