Exponential synchronization control of delayed memristive neural network based on canonical Bessel-Legendre inequality

<abstract><p>In this paper, we study the exponential synchronization problem of a class of delayed memristive neural networks(MNNs). Firstly, a intermittent control scheme is designed to solve the parameter mismatch problem of MNNs. A discontinuous controller with two tunable scalars is designed, and the upper limit of control gain can be adjusted flexibly. Secondly, an augmented Lyaponov-Krasovskii functional(LKF) is proposed, and vector information of N-order canonical Bessel-Legendre(B-L) inequalities is introduced. LKF method is used to obtain the stability criterion to ensure exponential synchronization of the system. The conservatism of the result decreases with the increase of the order of the B-L inequality. Finally, the effectiveness of the main results is verified by two simulation examples.</p></abstract>

A Dynamic AES Encryption Based on Memristive Chaos Neural Network

This paper proposes an Advanced Encryption Standard (AES) encryption technique based on memristive neural network. A memristive chaotic neural network is constructed by the use of the nonlinear characteristics of the memristor. The chaotic sequence, which is sensitive to the initial value and has good random characteristics, is used as the initial key of AES grouping to realize "one-time-one-secret" dynamic encryption. Results show that the algorithm has higher security, larger key space and stronger robustness than the conventional AES. It can effectively resist the initial key fixed and exhaustive attacks.

Application of a Memristive Neural Network for Classification of COVID-19 Patients

The global pandemic of COVID-19 has affected the lives of millions around the globe. We learn new facts about this corona virus every day. A contribution to this knowledge is described in the paper and it is related to employment of memristor neural networks and algorithms that help us analyze patients’ data and determine what patients are at increased risk for developing severe medical conditions once infected with the COVID-19. An efficient separation of potential patients in ill and healthy sub-groups is conducted using software and hardware neural networks, machine learning and unsupervised clustering. In the recent years, many works are related to reducing of neural chips area for the hardware realization of neural networks. For this purpose, a partial replacement of CMOS transistors in neural networks by memristors is made. Some of the main memristor advantages are its lower power consumption, nano-scale sizes, sound memory effect and a good compatibility to CMOS technology. In this reason, the main purpose of this paper is application of a memristor-based neural network with tantalum oxide memristor synapses for COVID-19 analysis. Additional experiments with data clustering are conducted. Experiments show that in fact patients with specific underlying health conditions and indicators are more predisposed to develop severe COVID-19 illness. This research is helpful for engineers and scientists to easier identifying patients that would need medical help

HPM: High-Precision Modeling of a Low-Power Inverter-Based Memristive Neural Network

In this paper, we propose a high-precision memristive neural network with neurons implemented by complementary metal oxide semiconductor (CMOS) inverters. Regarding the process variations in the memristors and the sensitivity of the memristive crossbar structure to these fluctuations, the read operation with repetitive pulses and feedback-based write in the memristors are used to implement the neural networks trained by the ex-situ method. Moreover, accurate modeling of the neuron circuit (CMOS inverter) and decreasing the mismatch between trained weights and the limited memristances fill the gap between simulation and implementation. To employ physical constraints based on the memristor framework during the training phase, a linear function is utilized to map the trained weights to the acceptable range of memristances after the training phase. To solve the vanishing gradient problem due to the use of the tanh function as an activation function and for better learning of the network, some measures are taken. Moreover, fin field-effect transistor (FinFET) technology is used to prevent the reduction of the accuracy of the inverter-based memristive neural networks due to the process variations. Overall, our implementation improves the speed, area, power-delay product (PDP), and mean square error (MSE) of the training stage by 91.43%, 95.06%, 48.29% and 81.64%, respectively.

Object Recognition Based on Nano Memristive Neural Network with Variable Structure

Many Memristive neural network arrays that have been designed in recent years are simultaneously dealt with all of their synapses in working status. Therefore, when a relatively small-scale neural network is implemented with these memristor arrays, some of these synapses which are not used may cause errors in the result due to the impact of unexpected interference signals, and it can also cause some unnecessary energy consumption. In this paper, a memristive neural network with variable network structure is investigated. Based on this network, the number of synapses involved in the work can be flexibly adjusted to improve system performance. Two different scales of neural networks are simulated in Pspice to prove the feasibility and effectiveness of the proposed memristive neural network structure.