METHODS AND MEANS OF AUTOMATED CONTROL OF MEMRISTOR STRUCTURES

2021 ◽  
pp. 24-33
Author(s):  
B. V. Artem'ev ◽  
I. B. Artem'ev ◽  
A. I. Vlasov ◽  
V. P. Zhalnin
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Memory Devices ◽  
Review Of The Literature ◽  
Electrical Parameters ◽  
Automated Control ◽  
Measuring Complex ◽  
The Stability

The article discusses some methods and means of automated control of memristive structures, which are currently very promising elements for creating new memory devices, as well as neural networks. A review of the literature on this topic is carried out. The description of the measuring complex developed by the authors is given. The measuring complex was used to measure the electrical parameters of the memristor structures. Experimental data are presented, which made it possible to propose a modification of the memristor structure in order to increase the stability of its operation.

scholarly journals Identifying nonclassicality from experimental data using artificial neural networks

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Valentin Gebhart ◽  
Martin Bohmann ◽  
Karsten Weiher ◽  
Nicola Biagi ◽  
Alessandro Zavatta ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

Accurate Modeling of Complex Antitoxin Effect of Quercetin Based on Neural Networks

2019 ◽  
Vol 29 (01) ◽  
pp. 1950013 ◽  
Author(s):  
Changju Yang ◽  
Entaz Bahar ◽  
Hyonok Yoon ◽  
Hyongsuk Kim
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Secondary Metabolites ◽  
Hela Cell ◽  
Protective Effect ◽  
Nonlinear Modeling ◽  
Inflammatory Activity ◽  
High Complexity ◽  
Anti Oxidant

A nonlinear modeling of the protective effect of Quercetin (QCT) against various Mycotoxins (MTXs) has a high complexity and is conducted using artificial neural networks (ANNs). QCT is known to possess strong anti-oxidant, anti-inflammatory activity that can prevent many diseases. MTXs are highly toxic secondary metabolites that are capable of causing disease and death in humans and animals. The protective model of QCT against various MTXs (Citrinin, Patulin and Zearalenol) on HeLa cell is built accurately via learning of sparsely measured experimental data by the ANNs. It has shown that the neuro-model can predict the nonlinear protective effect of QCT against MTX-induced cytotoxicity for the measurement of percentage of inhibition of MTXs.

scholarly journals Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lei Ding ◽  
Hong-Bing Zeng ◽  
Wei Wang ◽  
Fei Yu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Improved Stability ◽  
The Stability ◽  
Delay Dependent ◽  
Delay Dependent Stability ◽  
Varying Delay

This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.

scholarly journals Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Guiying Chen ◽  
Linshan Wang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Robust Stability ◽  
Sufficient Conditions ◽  
Leakage Term ◽  
Interval Neural Networks ◽  
Global Exponential Robust Stability ◽  
The Stability ◽  
Delay Differential ◽  
Delay Differential Inequality

The stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method ofM-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks. The results in this paper extend the corresponding conclusions without leakage delay. An example is given to illustrate the effectiveness of the obtained results.

Exponential stability and periodicity of memristor-based recurrent neural networks with time-varying delays

2017 ◽  
Vol 10 (02) ◽  
pp. 1750027 ◽  
Author(s):  
Wei Zhang ◽  
Chuandong Li ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Functional Approach ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Matrix Inequalities ◽  
Inclusion Theory ◽  
The Stability

In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.

scholarly journals Effects of Maximal Sodium and Potassium Conductance on the Stability of Hodgkin-Huxley Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yue Zhang ◽  
Kuanquan Wang ◽  
Yongfeng Yuan ◽  
Dong Sui ◽  
Henggui Zhang
Keyword(s):  
Experimental Data ◽  
Mathematical Method ◽  
Bifurcation Point ◽  
Potassium Conductance ◽  
Maximal Conductance ◽  
Computing Model ◽  
The World ◽  
Stability And Bifurcations ◽  
The Stability ◽  
Sodium And Potassium

Hodgkin-Huxley (HH) equation is the first cell computing model in the world and pioneered the use of model to study electrophysiological problems. The model consists of four differential equations which are based on the experimental data of ion channels. Maximal conductance is an important characteristic of different channels. In this study, mathematical method is used to investigate the importance of maximal sodium conductanceg-Naand maximal potassium conductanceg-K. Applying stability theory, and takingg-Naandg-Kas variables, we analyze the stability and bifurcations of the model. Bifurcations are found when the variables change, and bifurcation points and boundary are also calculated. There is only one bifurcation point wheng-Nais the variable, while there are two points wheng-Kis variable. The (g-Na,  g-K) plane is partitioned into two regions and the upper bifurcation boundary is similar to a line when bothg-Naandg-Kare variables. Numerical simulations illustrate the validity of the analysis. The results obtained could be helpful in studying relevant diseases caused by maximal conductance anomaly.

Measurement of Angular Acceleration of a Rigid Body Using Linear Accelerometers

1975 ◽  
Vol 42 (3) ◽  
pp. 552-556 ◽  
Author(s):  
A. J. Padgaonkar ◽  
K. W. Krieger ◽  
A. I. King
Keyword(s):  
Experimental Data ◽  
Rigid Body ◽  
Simple Procedure ◽  
Three Dimensional ◽  
Angular Acceleration ◽  
Theory And Practice ◽  
Body Segments ◽  
Impact Biomechanics ◽  
The Stability ◽  
Definition Of

The computation of angular acceleration of a rigid body from measured linear accelerations is a simple procedure, based on well-known kinematic principles. It can be shown that, in theory, a minimum of six linear accelerometers are required for a complete definition of the kinematics of a rigid body. However, recent attempts in impact biomechanics to determine general three-dimensional motion of body segments were unsuccessful when only six accelerometers were used. This paper demonstrates the cause for this inconsistency between theory and practice and specifies the conditions under which the method fails. In addition, an alternate method based on a special nine-accelerometer configuration is proposed. The stability and superiority of this approach are shown by the use of hypothetical as well as experimental data.

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