6 research outputs found
On the validity of memristor modeling in the neural network literature
An analysis of the literature shows that there are two types of
non-memristive models that have been widely used in the modeling of so-called
"memristive" neural networks. Here, we demonstrate that such models have
nothing in common with the concept of memristive elements: they describe either
non-linear resistors or certain bi-state systems, which all are devices without
memory. Therefore, the results presented in a significant number of
publications are at least questionable, if not completely irrelevant to the
actual field of memristive neural networks
A neural network approach towards generalized resistive switching modelling
Funding: This research was funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT—Portuguese Foundation for Science and Technology under project number DFA/BD/8335/2020.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Resistive switching behaviour has been demonstrated to be a common characteristic to many materials. In this regard, research teams to date have produced a plethora of different devices exhibiting diverse behaviour, but when system design is considered, finding a ‘one-model-fits-all’ solution can be quite difficult, or even impossible. However, it is in the interest of the community to achieve more general modelling tools for design that allows a quick model update as devices evolve. Laying the grounds with such a principle, this paper presents an artificial neural network learning approach to resistive switching modelling. The efficacy of the method is demonstrated firstly with two simulated devices and secondly with a 4 µm2 amorphous IGZO device. For the amorphous IGZO device, a normalized root-mean-squared error (NRMSE) of 5.66 × 10−3 is achieved with a [2, 50, 50, 1] network structure, representing a good balance between model complexity and accuracy. A brief study on the number of hidden layers and neurons and its effect on network performance is also conducted with the best NRMSE reported at 4.63 × 10−3 . The low error rate achieved in both simulated and real-world devices is a good indicator that the presented approach is flexible and can suit multiple device types.publishersversionpublishe
A switching control for finite-time synchronization of memristor-based BAM neural networks with stochastic disturbances
This paper deals with the finite-time stochastic synchronization for a class of memristorbased bidirectional associative memory neural networks (MBAMNNs) with time-varying delays and stochastic disturbances. Firstly, based on the physical property of memristor and the circuit of MBAMNNs, a MBAMNNs model with more reasonable switching conditions is established. Then, based on the theory of Filippov’s solution, by using Lyapunov–Krasovskii functionals and stochastic analysis technique, a sufficient condition is given to ensure the finite-time stochastic synchronization of MBAMNNs with a certain controller. Next, by a further discussion, an errordependent switching controller is given to shorten the stochastic settling time. Finally, numerical simulations are carried out to illustrate the effectiveness of theoretical results