Solving an Infectious Disease Model considering Its Anatomical Variables with Stochastic Numerical Procedures

The aim of the current work is to perform the numerical investigation of the infectious disease based on the nonlinear fractional order prey-predator model using the Levenberg–Marquardt backpropagation (LMB) based on the artificial neuron networks (ANNs), i.e., LMBNNs. The fractional prey-predator model is classified into three categories, the densities of the susceptible, infected prey, and predator populations. The statistics proportions for solving three different variations of the infectious disease based on the fractional prey-predator model are designated for training 80% and 10% for both validation and testing. The numerical actions are performed using the LMBNNs to solve the infectious disease based on the fractional prey-predator model, and comparison is performed using the database Adams–Bashforth–Moulton approach. The infectious disease based on the fractional prey-predator model is solved using the LMBNNs to reduce the mean square error (M.S.E). In order to validate the exactness, capability, consistency, and competence of the proposed LMBNNs, the numerical procedures using the correlation, M.S.E, regression, and error histograms are drawn.

Pure voltage-driven spintronic neuron based on stochastic magnetization switching behaviour

Voltage-driven stochastic magnetization switching in a nanomagnet has attracted more attention recently with its superiority in achieving energy-efficient artificial neuron. Here, a novel pure voltage-driven scheme with ~27.66 aJ energy dissipation is proposed, which could rotate magnetization vector randomly using only a pair of electrodes covered on the multiferroic nanomagnet. Results show that the probability of 180° magnetization switching is examined as a sigmoid-like function of the voltage pulse width and magnitude, which can be utilized as the activation function of designed neuron. Considering the size errors of designed neuron in fabrication, it’s found that reasonable thickness and width variations cause little effect on recognition accuracy for MNIST hand-written dataset. In other words, the designed pure voltage-driven spintronic neuron could tolerate size errors. These results open a new way toward the realization of artificial neural network with low power consumption and high reliability.

Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

Cloud computing systems revolutionized the Internet, and web systems in particular. Quality of service is the basis of resource configuration management in the cloud. Load balancing mechanisms are implemented in order to reduce costs and increase the quality of service. The usage of those methods with adaptive intelligent algorithms can deliver the highest quality of service. In this article, the method of load distribution using neural networks to estimate service times is presented. The discussed and conducted research and experiments include many approaches, among others, application of a single artificial neuron, different structures of the neural networks, and different inputs for the networks. The results of the experiments let us choose a solution that enables effective load distribution in the cloud. The best solution is also compared with other intelligent approaches and distribution methods often used in production systems.

New application of artificial neural network-based direct power control for permanent magnet synchronous generator

Purpose. This article proposes a new strategy for Direct Power Control (DPC) based on the use of Artificial Neural Networks (ANN-DPC). The proposed ANN-DPC scheme is based on the replacement of PI and hysteresis regulators by neural regulators. Simulation results for a 1 kW system are provided to demonstrate the efficiency and robustness of the proposed control strategy during variations in active and reactive power and in DC bus voltage. Methodology. Our strategy is based on direct control of instant active and reactive powers. The voltage regulator and hysteresis are replaced by more efficient and robust artificial neuron networks. The proposed control technique strategy is validated using MATLAB / Simulink software to analysis the working performances. Results. The results obtained clearly show that neuronal regulators have good dynamic performances compared to conventional regulators (minimum response time, without overshoots). Originality. Regulation of continuous bus voltage and sinusoidal currents on the network side by using artificial neuron networks. Practical value. The work concerns the comparative study and the application of DPC based on ANN techniques to achieve a good performance control system of the permanent magnet synchronous generator. This article presents a comparative study between the conventional DPC control and the ANN-DPC control. The first strategy based on the use of a PI controller for the control of the continuous bus voltage and hysteresis regulators for the instantaneous powers control. In the second technique, the PI and hysteresis regulators are replaced by more efficient neuronal controllers more robust for the system parameters variation. The study is validated by the simulation results based on MATLAB / Simulink software.

A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring. Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites, leading to a very low gas adsorption ability. Moreover, the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior. In this work, a gas sensing structure model based on CuS quantum dots/Bi2S3 nanosheets (CuS QDs/Bi2S3 NSs) inspired by artificial neuron network is constructed. Simulation analysis by density functional calculation revealed that CuS QDs and Bi2S3 NSs can be used as the main adsorption sites and charge transport pathways, respectively. Thus, the high-sensitivity sensing of NO2 can be realized by designing the artificial neuron-like sensor. The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable, which can enhance the NO2 sensitivity due to the rich sensitive sites and quantum size effect. The Bi2S3 NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission. The neuron-like sensor that simulates biological smell shows a significantly enhanced response value (3.4), excellent responsiveness (18 s) and recovery rate (338 s), low theoretical detection limit of 78 ppb, and excellent selectivity for NO2. Furthermore, the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.

Numerical Investigations through ANNs for Solving COVID-19 Model

The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.

Backstepping Control of Switched Reluctance Motor with Artificial Neural Network based Flux Estimator

The paper presents a new approach to design a nonlinear controller for switched reluctance motors (SRMs) based on backstepping technique and artificial neuron network (ANN) in flux estimator. Backstepping controller with an ANN flux estimator will be applied for controlling SRMs which have a nonlinear drive model. The ANN flux estimator was trained off-line using backpropagation algorithm. The stability of the closed control loop was analyzed and proved accroding to the Lyapunov stability standard. The numerical simulation results confirmed the accuracy of the estimator and the quality of the backstepping control system.

Synthesis of four new neuro-statistical tests for testing the hypothesis of independence of small samples of biometric data

The paper considers the analysis of small samples according to several statistical criteria to test the hypothesis of independence, since the direct calculation of the correlation coefficients using the Pearson formula gives an unacceptably high error on small biometric samples. Each of the classical statistical criteria for testing the hypothesis of independence can be replaced with an equivalent artificial neuron. Neuron training is performed based on the condition of obtaining equal probabilities of errors of the first and second kind. To improve the quality of decisions made, it is necessary to use a variety of statistical criteria, both known and new. It is necessary to form networks of artificial neurons, generalizing the number of artificial neurons that is necessary for practical use. It is shown that the classical formula for calculating the correlation coefficients can be modified with four options. This allows you to create a network of 5 artificial neurons, which is not yet able to reduce the probability of errors in comparison with the classical formula. A gain in the confidence level in the future can only be obtained when using a network of more than 23 artificial neurons, if we apply the simplest code to detect and correct errors.

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.