5 citations · 5 across the 5 of their papers we have counts for
5 papers
Mixed Potential Approach to Convergence of Nonlinear RLC Circuits with Memristors
Mauro Di Marco, Mauro Forti, Luca Pancioni +2
The paper considers a large class of nonlinear circuits, termed RLCM, containing all four basic circuit elements, i.e., resistors, inductors, capacitors and memristors. A companion…
Mixed potential for nonlinear RLC circuits with memristors
Mauro Di Marco, Mauro Forti, Luca Pancioni +2
In two seminal articles published in 1964, Brayton and Moser introduced the concept of a mixed potential as a fundamental theoretic tool to describe and analyze a class RLC of nonl…
Convergent Weight and Activation Dynamics in Memristor Neural Networks
Mauro Di Marco, Mauro Forti, Luca Pancioni +2
Convergence of dynamic feedback neural networks (NNs), as the Cohen-Grossberg, Hopfield and cellular NNs, has been for a long time a workhorse of NN theory. Indeed, convergence in…
Embedding classic chaotic maps in simple discrete-time memristor circuits
Mauro Di Marco, Mauro Forti, Giacomo Innocenti +2
In the last few years the literature has witnessed a remarkable surge of interest for chaotic maps implemented by discrete-time (DT) memristor circuits. This paper investigates on…
Physical Implementation of a Tunable Memristor-based Chua's Circuit
Manuel Escudero, Sabina Spiga, Mauro di Marco +5
Nonlinearity is a central feature in demanding computing applications that aim to deal with tasks such as optimization or classification. Furthermore, the consensus is that nonline…