Neural Network Mathematical Model Research Articles

Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor

Abstract In this paper, a method of generating multi-double scroll attractors is proposed based on the memristor Hopfield neural network (HNN) under pulse control. First, the original hyperbolic-type memristor is added to the neural network mathematical model, and the influence of this memristor on the dynamic behavior of the new HNN is analyzed. The numerical results show that after adding the memristor, the abundant dynamic behaviors such as chaos coexistence, period coexistence and chaos period coexistence can be observed when the initial value of the system is changed. Then the logic pulse is added to the external memristor. It is found that the equilibrium point of the HNN can multiply and generate multi-double scroll attractors after the pulse stimulation. When the number of logical pulses is changed, the number of multi-double scroll attractors will also change, so that the pulse can control the generation of multi-double scroll attractors. Finally, the HNN circuit under pulsed stimulation was realized by circuit simulation, and the results verified the correctness of the numerical results.

Neural network model development for detecting atypical mitoses in histological slides

Background: Modern computer systems allow digitizing and examining images of histological preparations, which led the authors to the idea of using machine learning tools in digital pathohistology. The ability of neural networks to find sub-visual image features in digitized histological preparations provides the basis for better qualitative and quantitative image analysis. Existing machine learning methods provide good accuracy and speed in recognizing various images, which gives hope for their wide application, including in oncologic diagnostics. AIM: Use methods of mathematical modeling to identify pathological mitoses in histological preparations as the main sign of the difference between malignant and benign tumor growth. MATERIALS AND METHODS: Histological images of the N.N. Priorov National Medical Research Center of Traumatology and Orthopedics were used as a data set for the neural network model. The model was tested using 188 histologic slides from 67 patients treated at the institute. Histological preparations were scanned on a Leica Aperio CS2 microscope with a ×400 resolution and converted into JPEG format with further processing. Next, the test images were analyzed in streaming mode using the created neural network model in order to obtain the coordinates of the desired diagnostic object — pathological mitosis and the probability with which the model found the object of this category. The obtained images were analyzed by a pathologist to determine whether the detected object corresponded to pathological mitosis. RESULTS: The authors have chosen an architecture, developed a methodology for training a neural network, and created a model that can be used to detect pathologic mitoses in histologic preparations. The authors do not attempt to replace the physician, but show the possibility of an integrated approach to data analysis by a computer system and a pathologist. Conclusions: The developed mathematical model of neural network used as a part of technological solution for recognizing pathological mitoses in scanned histological preparations can be used as a tool to reduce the time of research and increase the accuracy of diagnosis by a pathologist.

OPTIMIZATION OF CYBER-PHYSICAL SYSTEM PARAMETERS BASED ON INTELLIGENT IOT SENSORS DATA

The optimization of cyber-physical systems (CPS) parameters is researched with focusing on the integration of computation, networking, and physical processes with the Internet of Things (IoT). The use of intelligent IoT sensors is crucial for collecting real-time data, which is necessary for enhancing the efficiency, reliability, and performance of CPS. Various methods of CPS parameters optimization are analyzed and categorized into model-based approaches, data-driven approaches, and hybrid approaches. The model-based approaches rely on mathematical models to describe CPS behavior and use optimization algorithms like linear programming and evolutionary algorithms to predict system responses and optimize parameters. But, the limitations of model-based approaches are related to complex systems with uncertain or dynamic behavior. The data-driven approaches are more suitable for complex cyber-physical systems. These approaches utilize machine learning and data analytics techniques to extract patterns from sensor data, which are then used to adjust system parameters. The hybrid approaches combine elements of both model-based and data-driven methods. The method of cyber-physical system parameters optimization based on intelligent IoT sensors data processing is developed with using of distributed neural network. The optimization problem is formulated with constraints for the system parameters. The neural network mathematical model and learning algorithm are proposed. The performed research shows the importance of developing optimization methods for CPS parameters based on intelligent IoT sensor data, considering the evolving nature of IoT technology. The integrating intelligent sensors into CPS offers new opportunities for optimizing system performance but also presents challenges in data management and security that should be addressed in future.

A ‘Systemic’ Model of a Mind-Body Interaction

This paper shows that, by using a “systemic” mind theory and a formal model initially proposed by Neisser, it is possible to formally represent the mind-body communication processes as they are expressed within the psychoanalytic environment. In this paper, mathematical models of neural networks are used to demonstrate that Neisser’s hypothesis is reinforced if a temporal evolution appears. On the contrary, additional neurophysiological mechanisms must be postulated to show the differences between primary and secondary processes.

Forecasting financial markets using the random forest algorithm

The article provides material on the analysis of the financial market using the random forest algorithm. The general problem of forecasting financial markets and the role of modern technologies for accurate forecasts and automation of trading strategies are considered. An overview of existing forecasting models and the possibility of their application for financial markets was conducted. The latest studies and publications were analyzed, on the basis of which a research program was developed. The created program has a modular structure and represents a library that can be used for further research. A neural network was built and trained, which, using a random forest algorithm, can perform analysis over a certain period and provide predictions. As part of the research, open data and an analysis of the Apple company were used. During the experiments, an analysis of the accuracy of the model under the influence of hyperparameters, an analysis of the model's operation under different market conditions, a comparison with other forecasting methods, and an analysis of the impact of the amount of data on the accuracy of the model were made. Several neural network mathematical models were built for market forecasting. After that, they were trained on the selected datasets. For training, information was taken for a long period of time, data for quarters, daily profit, profit for a year. The software was written in Python, a number of libraries were used, namely yfinance, Sklearn-Scikit-learn, Matplotlib.pyplot, Pandas. Experimental studies compared the results of different approaches to analysis. Models using the random forest method and the linear regression model were compared, proving the feasibility of using the random die method for this type of problem. With the help of graphs, the metrics are demonstrated and the root mean square errors are derived. To determine the adequacy of the work of the developed neural network, testing was conducted to identify errors when compared with other markets

The growing use and impact of artificial intelligence technologies in the tourism industry

The research relevance is determined by the increasing role of artificial intelligence in the tourism business, which was demonstrated during the outbreak of the COVID-19 pandemic in Bali, resulting in a decline in the number of tourists visiting the island. The research aims to investigate the real-life experience of applying artificial intelligence systems in the tourism industry to address the challenges of developing the tourism industry in Bali, Indonesia. The basis of the methodological approach is a combination of theoretical methods of analysis and synthesis of the collected information covering the practical experience of the application of artificial intelligence systems in the tourism industry, as well as a survey of customers of travel agencies that have used such systems in trips to Bali. The research results show that artificial intelligence can significantly help the tourism business in Bali, as the number of tourists visiting the island dropped by 40% during the pandemic, resulting in a reduction in the number of tourism staff. The practical application of systems based on artificial intelligence opens up a wide range of business opportunities, as it allows for optimizing the activities of tourism firms and providing customers with high-quality services with highly technical equipment. The use of neural network mathematical models of tourist firms’ activity based on AI, allows them to achieve high speed of data processing, which allows tourist firms numerous competitive advantages and significantly improves the quality of their services. The practical significance of the results of this scientific research lies in the possibility of their application in the organization of the activities of enterprises in the sphere of tourism business, built on the use of digital technologies and systems based on artificial intelligence.

Prevention of PID Phenomenon for Solar Panel Based on Mathematical Data Analysis Models

In recent years, the problem of potential-induced degradation (PID) phenomenon has been deeply associated with solar power issues because it causes serious power attenuation of solar panels and results in lowering its power generation efficiency. Thus, effectively identifying the PID problem from insights of industry data analysis to reduce production costs and increase the performance of power generation is an interesting and important subject for the solar power industry. Moreover, by the traditional standard rule (IEC62804) and the condition of a 96 h testing time, the costs of testing time and assembling materials against PID are very high and must be improved. Given the above reasons, this study proposes a hybrid procedure to organizes four mathematical methods: the mini-module testing, solar cell testing, a settling time, and a neural network, which are named as Method-1–Method-4, respectively, to efficiently solve the PID problem. Consequently, there are four key outcomes from the empirical results for solar power application: (1) In Method-1 with a 96 h testing time, it was found that the large module with higher costs and the mini module with lower costs have a positive correlation; thus, we can replace the large-module testing by the effective mini module for lower cost on module materials. (2) In Method-2 with a 24 h testing time, it was also found that the mini module and the solar cell are positively correlated; this result provides evidence that we can conduct the PID test by the easier solar cell to lower the costs. (3) In Method-3, the settling time achieves an average accuracy of 94% for PID prediction with a 14 h testing time. (4) In Method-4, the experimental result provides an accuracy of 80% when identifying the PID problem with the mathematical neural network model and are obtained within a 2 h testing time. From the above results, these methods succeed in reducing cost of materials and testing time during the manufacturing process; thus, this study has an industrial application value. Concurrently, Method-3 and Method-4 are rarely seen in the limited literature review for identifying PID problem; therefore, this study also offers a novel contribution for technical application innovation.

Analytical and Numerical Study of Information Retrieval Method Based on Single-Layer Neural Network with Optimization of Computing Algorithm Performance

This work presents a mathematical model of a fast-acting single-layer artificial neural network applied to the task of image reconstruction after noise. For research purposes, this algorithm was implemented in the Python and C++ programming languages. The numerical simulation of the recovery efficiency of the described neural network was performed for different values of the noise factor, the number of samples required to train elements in the sample and the dimensionality of the coupling coefficients, w. The study of the mathematical model of this neural network is presented; as a result, it is possible to identify its essence, to reduce the number of operations required to recover a single element and to increase recovery accuracy by changing the order of calculation of coupling coefficients, w.

THE USE OF ARTIFICIAL NEURAL NETWORKS ON RUSSIAN RAILWAYS TO CONTROL CURRENT COLLECTORS OF TRAINS

Background. This article presents the results of the analysis of the mechanism of artificial neural networks on Russian railways for the control of current collectors of trains. A critical assessment of the situation was carried out and a forecast of the further development of neural network technologies in railway transport was given.
 Materials and/or research methods. Methods of mathematical modeling, system analysis, data analysis, comparison, systems theory, as well as architecture and mathematical model of neural network and information standards were used.
 Results. A model of the information system for monitoring the characteristics of current collectors has been developed, the algorithm of its operation and the functionality of the software package of the model of the system for monitoring the characteristics of current collectors of rolling stock have been described. The algorithm of neural network operation for determining the characteristics of electric rolling stock current collectors is presented. The analysis of the prospects for the use of artificial neural networks in the field of railway transport has been carried out, and a simulation model of an information system for monitoring the characteristics of current collectors of electric rolling stock of railway transport has been created.
 Conclusion. The article examines the positive experience of JSC “Russian Railways” on the introduction of neural network technologies in technological processes and further prospects for the use of neural network technologies on Russian railways. The necessity of intensifying the development and implementation of neurotechnologies to solve a wide range of tasks, including tasks to optimize the organization of maintenance and repair of rolling stock, is noted. The effectiveness and relevance of the use of artificial neural networks in various fields of activity, especially on Russian railways, is shown, and a model of an information system in the field of monitoring the characteristics of current collectors is proposed.

The Use of Intelligent Mathematical Models for Regional Investment Distribution Processes Analysis

In this article, the artificial neural network mathematical model is used for Regional Investment Distribution Processes Analysis in the regions of the Republic of Uzbekistan; forecasts are made using this model, and the results are compared with the results determined using the trend and panel methods, and the preferred method is defined. Multi-layer perceptron, radial-basis grid, generalized-regression grid, and recurrent grid can be used to solve the forecasting problem. In the study, a program for intellectual analysis and forecasting of socio-economic development indicators of the regions of the Republic of Uzbekistan using the generalized regression network was developed. This program makes it possible to extract the most necessary factors from other methods even in the presence of multi-factor indicators and determine the future forecast result under their influence. According to the results of the forecast determined using the intellectual mathematical model developed because of the research, by 2025, the volume of the gross regional product of Andijan region is expected to be equal to 95607.34 billion soums, and in Samarkand region, it is expected to be equal to 80419.73 billion soums, in Tashkent city it is expected to be 259301.8 billion soums. According to the error levels of the results determined by the intellectual mathematical method, it represents an average error of 1.13% compared to the forecast period. If we determine the result of the trend by the level of error, it is equal to an average of 4.96% error compared to the forecast period, and these results prove the superiority of the intellectual mathematical method developed in the research.

APPLICATION OF THE MODERN EVALUATION’S METHOD OF SOCIO-ECONOMIC POTENTIAL OF REGIONS

In this article, the programs of artificial neural network mathematical models are recommended, which are a new direction for analysis of problems arising in the distribution of investments in the regions of the Republic of Uzbekistan; forecasts are made using this program, and the results are compared with the results determined using the trend and panel methods, and the preferred method is defined

Shutdown idling performance of the nuclear main coolant pump under station blackout accident: An optimization study

Lead-cooled fast reactor (LFR) is an important reactor type of the Generation IV nuclear energy, and the main coolant pump (MCP) is the only and most critical power equipment in primary circuit of LFR. The shutdown idling performance of the MCP, is one of the most important safety indicators in the event of a station blackout accident. When the motor loses its power supply, the MCP needs to run under the inertia of the flywheel to ensure the coolant flow for a short time. This study mainly focuses on the idling mathematical model of the MCP under power failure accidents. In this study, taking the shutdown idling performance of the MCP as the optimization goal, the parametric optimization analysis of the impeller and diffuser structure of the MCP was carried out. Specifically, based on the platform ISIGHT, the software of CFturbo, TurboGrid, CFX, Matlab and FLOWMASTER are systematically integrated, the automated 3D modeling, automatic meshing and automatic CFD calculation of different hydraulic model of the MCP are realized. And the neural network mathematical model between the geometric parameters of MCP and the idling performance indicators is established. The study shows that there is a strong linear relationship between the idling performance of the MCP and its head and shaft power, and meridian parameters of Δβ 2, Z 1, Φ 1h and the radial surface parameters of Δβ 1 and Δβ 2, Z 1 and Φ 1h have the greatest impact on the idling performance of MCP.

Analysis of Erhu Performance Effect in Public Health Music Works Based on Artificial Intelligence Technology.

With the rise of Erhu teaching in recent years, a large number of people have joined the team to learn Erhu playing. However, due to the high cost of teaching and the unique one-to-one teaching mode between teachers and students, Erhu education resources are very scarce. Learning Erhu performance has become a luxury activity. Nowadays, with the rise of artificial intelligence, computer music is developing rapidly. Music has two important aspects: composition and performance. Different kinds of instruments convey different styles, and players inject different rhythms and dynamics into their performance, thus producing rich expressive force. The development of image style conversion, which opens people's evaluation of music performance, is an important issue in many fields of artificial intelligence (it is also known as intelligence, machine intelligence, referring to the intelligence shown by the machine made by people. Usually, artificial intelligence refers to the technique of presenting human intelligence through ordinary computer programs). For an Erhu song, there are various factors that affect its effectiveness, and there are many indexes to evaluate it, such as sense of rhythm, expressive force, musical sense, style, and so on. Using a computer to simulate the evaluation process is essential to find out the mathematical relationship between the factors that affect the performance of music and the evaluation indexes. Neural network is a kind of mathematical model proposed by simulating the way of thinking of human brain in artificial intelligence. It has the advantages of not having strict requirements on data distribution, nonlinear data processing method, strong robustness, and dynamics and is very suitable for the mathematical model of evaluation system. In addition, the neural network also has a strong theoretical basis, and their application in various industries has developed basically mature. This paper tries to introduce a deep neural network mathematical model into the evaluation system of Erhu performance, and the experimental results prove the reliability and practicality of the method in this paper. It can provide a method basis and theoretical reference for evaluation of Erhu performance effect.

Web Application for Statistical Tracking and Predicting the Evolution of Active Cases with the Novel Coronavirus (SARS-CoV-2)

Since February 2020, when the first case of infection with SARS COV-2 virus appeared in Romania, the evolution of COVID-19 pandemic continues to have an ascending allure, reaching in September 2020 a second wave of infections as expected. In order to understand the evolution and spread of this disease over time and space, more and more research is focused on obtaining mathematical models that are able to predict the evolution of active cases based on different scenarios and taking into account the numerous inputs that influence the spread of this infection. This paper presents a web responsive application that allows the end user to analyze the evolution of the pandemic in Romania, graphically, and that incorporates, unlike other COVID-19 statistical applications, a prediction of active cases evolution. The prediction is based on a neural network mathematical model, described from the architectural point of view.

Predictive analysis and multi objective optimization of wire-EDM process using ANN

Metal Matrix Composites (MMCs) plays very important role in structural, aerospace and automotive industries, because of their good properties like light weight, high specific strength and good wear resistance. Among various nonconventional machines, wire EDM is widely used in cutting of hard materials with good mechanical properties and high accuracy. So, in this paper, composite Al7075 + 10%Al2O3 is selected as material and stir casting process is used in composite fabrication and machining is done using Wire EDM. Increasing the Material Removal Rate (MRR) value and decreasing the Surface Roughness (SR) are the main objectives of this paper. There are five input parameters such as pulse-on, voltage; pulse-off, bed speed and current are considered to study the output parameters and to achieve the objectives. Performance analysis of the experimental values is done using Artificial Neural Network (ANN) mathematical model and the model is evaluated using R-square value. Artificial Neural Network model gives better and accurate results compared with linear regression model. In practical situation, it is hard to find the best for all the objectives simultaneously. Pareto optimal solutions are non-dominated solutions when all the objectives are considered. So, in this study, Solution for multi objective optimization is determined based on Pareto optimal optimization.

Obtaining a Neural Network Mathematical Model That Takes Into Account Various Power Supply Schemes

Gas turbine units are widely used as a drive for a synchronous generator in a gas turbine power plant. The main problem here lies in the fact that the control systems of such gas turbine plants are transferred practically unchanged from their aviation counterparts. This situation leads to inefficient operation of the gas turbine power plant, which affects the quality of electricity generation. To solve this problem, it is necessary to improve the control algorithms for the automatic control systems of gas turbine plants. When solving this problem, gas turbine plants should be considered in interaction with other subsystems and units; for gas turbine power plants, this is, first of all, an electric generator and the electric power system as a whole. Setting up a control system is one of the most costly stages of their production, both in terms of finance and time. Especially time-consuming operations are non-automated manual configuration management system for developmental and operational testing. Therefore, it is proposed to use a software-modeling complex, on the basis of which it is possible to obtain a neural network mathematical model of a gas turbine power plant and conduct its tests.

Predicting various forms of endometriosis using artificial neural networks

Introduction. Endometriosis is a difficult-to-diagnose pathology due to the diversity of clinical manifestations and the lack of high-precision markers necessary for rapid noninvasive diagnosis and timely administration of pathogenetically justified treatment.The aim of this work was to develop a computer system that allows us to assess the probability of endometriosis with various localizations in women, based on artificial neural networks.Material and Methods. The neural network mathematical models were constructed and tested based on data from 110 patients with morphologically pre-confirmed endometriosis. Patients were divided into training and test samples. The models were built based on anamnestic data and results of proteomic and enzyme immunoassays in blood plasma samples.Results and Discussion. In the course of the study, four mathematical models of neural networks were constructed to predict the presence or absence of endometriosis in a woman and its localization if present. Based on these mathematical models, a computer system “Differential diagnosis of endometriosis” was developed. This system allowed to assess the probability and localization of endometriosis in a patient based on parameters obtained as a result of neural network training.Conclusion. The developed computer diagnostic system allowed predicting the presence of endometriosis and its localization with a probability over 80%, depending on the predicted localization, based on data about the patient and the results of her examination. This system may be used for differential diagnosis of endometriosis from other diseases of the female reproductive system, as well as for differential diagnosis of various endometriosis localizations.

The application of tools of neural networks and artificial intelligence in the recreational sphere

The developed neural network mathematical model designed to assess the effectiveness of inbound tourism in various regions of the country. Using the mathematical model, the distribution of the significance of factors influencing the choice of tourists when visiting certain regions is revealed, which should be taken into account when adjusting the federal and regional policies in the field of tourism and recreational activities. The use of this kind of computer program will allow experts from relevant departments and travel agencies to increase the validity of managerial decisions and strategic developments. Thus, the competitiveness of the regions is enhanced and a new approach to the development of the recreational sphere is introduced.

Application of an artificial neural network for the improvement of agricultural drainage water quality using a submerged biofilter.

Artificial neural network (ANN) mathematical models, such as the radial basis function neural network (RBFNN), have been used successfully in different environmental engineering applications to provide a reasonable match between the measured and predicted concentrations of certain important parameters. In the current study, two RBFNNs (one conventional and one based on particle swarm optimization (PSO)) are employed to accurately predict the removal of chemical oxygen demand (COD) from polluted water streams using submerged biofilter media (plastic and gravel) under the influence of different variables such as temperature (18.00-28.50 °C), flow rate (272.16-768.96 m3/day), and influent COD (55.50-148.90 ppm). The results of the experimental study showed that the COD removal ratio had the highest value (65%) when two plastic biofilter media were used at the minimum flow rate (272.16 m3/day). The mathematical model results showed that the closeness between the measured and obtained COD removal ratios using the RBFNN indicates that the neural network model is valid and accurate. Additionally, the proposed RBFNN trained with the PSO method helped to reduce the difference between the measured and network outputs, leading to a very small relative error compared with that using the conventional RBFNN. The deviation error between the measured value and the output of the conventional RBFNN varied between + 0.20 and - 0.31. However, using PSO, the deviation error varied between + 0.058 and - 0.070. Consequently, the performance of the proposed PSO model is better than that of the conventional RBFNN model, and it is able to reduce the number of iterations and reach the optimum solution in a shorter time. Thus, the proposed PSO model performed well in predicting the removal ratio of COD to improve the drain water quality. Improving drain water quality could help in reducing the contamination of groundwater which could help in protecting water resources in countries suffering from water scarcity such as Egypt.

Application of a neural network mathematical model in the development of hot air roasting process technology for Chironji (Buchanania lanzan) kernels

The hot air roasting of Chironji kernels was performed at 25 different combinations of temperature and time. The influence of roasting parameters on bulk density (BD), hardness, water absorption capacity (WAC), water activity (WA), browning index (BI), total phenol content (TPC), and antioxidant activity (AA) were assessed. A decrease in BD, hardness, and WA, while an increase in WAC, BI, TPC, and AA was observed in roasted kernels. An Artificial Neural Network (ANN) model combined with the Genetic Algorithm (GA) was used for obtaining the optimum values of roasting parameters. The values of responses measured at the optimum conditions of 146°C temperature and 34 min were BD of 537 ± 1.21 kg/m3, hardness of 15.01 ± 0.31 N, WAC of 1.33 ± 0.11 g/g, WA of 0.0059 ± 0.007, BI of 74.62 ± 0.33, TPC of 17.68 ± 0.49 mg GAE/g dry wt, and AA of 74.09 ± 0.19%. Practical applications Value addition of minor forest produce like Chironji kernels can improve the livelihood of the rural populace, and roasted Chironji kernels can be a popular value-added product in the market. The results of this study can help the food processors for roasting of Chironji kernels while retaining its quality characteristics.