СИНЕРГЕТИЧНІ МЕТОДИ В ПРОГНОЗУВАННІ ДИНАМІКИ ПРОЦЕСІВ ФОНДОВИХ РИНКІВ

Integrated decision support for aviation safety inspectors

Through deep research on high frequency data storage problems and neural network technologies, a method of solving high frequency data storage problems is proposed, The method applies perceptron neural network and BP neural network technologies on distributed data high-frequency storage. It uses perceptron neural network to set up classification model which decides success or failure of high-frequency data storage, and uses BP neural network to predict the size of each client input data after increasing new clients in the distributed system. To verify the effectiveness of the method, it uses the actual input data of multiple clients as test and training data, and compares with exponential smoothing method. Simulation results show that the method solves the instability problems of distributed data high-frequency storage, and has good comprehensive performance.

Objective: The computer image processing and neural network technology are applied to diagnose the thermal energy of boiler plants, i. e., the flame combustion diagnosis, to verify their effectiveness and superiority. Methods: First, the YD-NQ type endoscopic high temperature video acquisition system is used to collect the images of flame combustion. Second, the images are pre-processed by the gray-scale method and the median filtering method. Then the flame combustion parameter features are extracted. The neural network algorithm is improved, and the boiler combustion model based on the improved neural network algorithm is established. Therefore, the combustion decision base is obtained. Finally, the improved neural network model is compared with the traditional neural network model and the 5-4 model to verify its validity. Results: The experiments have found that the improved neural network model is superior to the traditional neural network model. Meanwhile, its accuracy rate and confidence are relatively higher than those of the traditional model. In addition, a single sample also consumes shorter running time, which is 0.0075 seconds. Comparing with the 5-4 model, the improved neural network model has certain advantages, i. e., its accuracy rate and confidence are relatively higher, which are, respectively 91.28% and 96.69%, however, a single sample consumes longer running time than the 5-4 model. Conclusion: The experimental research has found that the application of computer image processing and neural network technology to the thermal energy diagnosis of boiler plants can effectively determine the stability of flame combustion, timely understand the state of flame combustion, and thus diagnose the thermal energy. Therefore, they have values for applications.

Years ago, many researchers proposed that chaos theory could provide a kind of universal theory of the qualitative behavior of all dynamical systems. Thus it could provide a solid mathematical foundation for unifying efforts to address the three really fundamental questions of basic science: (1) what is life?; (2) what is mind or intelligence?; (3) what are the underlying physical laws of the universe? Chaos theory is essentially a recent extension of a larger field of mathematics which it is part of: nonlinear system dynamics (NSD). It mainly addresses systems governed by Ordinary Differential Equations (ODE) or their discrete-time cousins. Because the vast bulk of models now used in computational neuroscience are ODE models, it is only natural that neuroscience has developed great interest in the use of NSD as a tool for analyzing and designing neural network models. Walter Freeman, in particular, has played a seminal role in describing how chaos theory can be used in computational neuroscience, in his classical models of the olfactory system, described in articles in Scientific American and many scientific journals and IJCNN proceedings. His colleague Morris Hirsch in mathematics has helped provide a solid foundation for this work. This work has helped to inspire many efforts throughout the world to use chaos as a design principle in computer systems and in artificial neural network (ANN) design. Recently, however, several issues or questions have begun to emerge, some at Berkeley and some in other places. The author discusses these questions and their implications.

Purpose. At the present stage, network attack detection systems based on the following neural networks are most often offered: multilayer perceptron, Kohonen network or self-organizing map and their combinations. The efficiency problem of two approaches to detect attacks on a computer network using neural network technology based on the normalized data of the open NSL-KDD database is considered. Methodology. As an architectural solution to the network attack detection system, it is proposed to consider the following approaches: based on one neural network determining the attack class (first approach) and an ensemble of five neural networks (second approach), which at the first stage determines the attack category (DoS, Probe, U2R , R2L), and in the second stage, the attack class belonging to a certain category. Findings. Based on the neural networks created in the MatLAB program, a study was conducted of their error on the length of the training sample using various training algorithms: Levenberg-Marquardt; Bayesian Regularization; Scaled Conjugate Gradient with different numbers of hidden neurons (minimum, average and maximum). Certain optimal parameters of neural networks with two approaches were determined. Originality. In the course of conducting experiments with various approaches, the results obtained were: TP (True Positive); FP (False Positive); FN (False Negative); TN (True Negative), based on which the following indicators were calculated for assessing the quality of solutions: correct determination of network attacks; false positives; reliability; accuracy and completeness, which prove the feasibility of using an ensemble of neural networks (second approach). Practical value. On the created neural networks with various approaches, studies were conducted: the operating time of neural networks; errors of the first kind; errors of the second kind. According to the results of the first study, the average operating time of an ensemble of neural networks is 0.92 s, and the operating time of a neural network (according to the first approach) is 2.21 s. According to the results of the second study, the error of the first kind using an ensemble of neural networks is 2.17%, and using the neural network (the first approach) – 7.39%. According to the results of the third study, the error of the second kind using an ensemble of neural networks is 3.91%, and using the neural network (the first approach) – 6.96%, which is confirmed by the efficiency of using an ensemble of neural networks (second approach).

The use of artificial intelligence in modeling different processes and systems have recently become an essential need. In this paper, artificial neural network, will be used to model a desalination system that uses reverse osmosis principle. The purity of the water will be predicted and calculated using neural network technology. This research aims to reveal the hidden patterns and relationships in data obtained from the reverse osmosis process, by training and testing the data set to enhance the prediction of extracting clean water. The performance of the artificial neural network was very promising as obtained from some of the metrics, like the high value of correlation factor between the actual output data and the output data obtained from the neural network model.

Network traffic is one of the most important actual indicators of the information and telecommunication system (ITS) of railway transport. Recent studies show that network traffic in the ITS of railway transport is self-similar (fractal), for the study of which the Hirst indicator can be used. One of the possible solutions is a method of network traffic forecasting using neural network technology, which will allow you to manage traffic in real time, avoid server overload and improve the quality of services, which confirms the relevance of this topic. The method of forecasting the parameters of network traffic in the ITS of railway transport using neural network technology is proposed: for long-term forecasting (day-ahead) of network traffic volume based on network traffic volumes for the previous three days using the created multilayer neuro-fuzzy network; for short-term prediction (one step forward, which takes five minutes) of network traffic intensity based on network traffic intensities for the previous fifteen minutes using the created multilayer neural network. The corresponding samples are formed on the basis of real values of network traffic parameters in the ITS of railway transport. Studies of optimal parameters of the created multilayer neural network, which can be integrated into specialized analytical servers of the ITS of railway transport, are carried out, which will provide a sufficiently high level of short-term forecasting of network traffic parameters (in particular intensity) in the ITS of railway transport at the stage of deepening the integration of the national transport network into the Trans-European Transport Network.

The system of preventive maintenance for various types of equipment is considered. To identify the need for managerial influence on the processes of maintenance and repair of the product, the coefficient of variation of the maintenance (repair) cycle is proposed. This coefficient is formed by the values of operating time or work between stops for maintenance or repair. To determine its numerical value, it is recommended to use such concepts as "maintenance (repair) cycle" and "frequency of maintenance (repair)". The values of the coefficient characterizing the stability of the process of repair and maintenance of the product are determined. These values of the coefficient are proposed to be used to justify the need to analyze the causes of failures or, in other words, the instability of the repair and maintenance process. The main managerial influences on the process of maintenance and repair are determined, which can be taken as a result of the analysis of the causes of failures. These include: the decision to search for a technical service provider, expanding the competencies of employees, updating the material base. Modern industry 4.0 tools allow you to get the required level of quality when working with a large data stream. The use of neural network technology in various technical service management tasks is due to some advantages over classical methods of statistical processing of results. The experiments of various authors are presented in which it is shown that neural network technologies are able to interpret graphic information, justify its use in technical diagnostics at the stage of analyzing diagnostic information in the form of oscillograms, which allows the use of complex diagnostic equipment in the absence of experts. A neural network simulation was carried out to recognize defects in bearing assemblies. As a result, the created neural network recognized the type of defect in accordance with the matrices of input signals and the output matrix of types of defects, which makes it possible to develop a mechanism for improving technical service using neural networks as a tool for analyzing diagnostic information in the absence of experts in this field.

Abstract: Stock market forecasting remains a major concern to investors, financial institutions, and traders. Most classical forecasting techniques such as statistical modelling and technical analysis generally fail to account for the complexity and non-linearity of financial markets. Recently, neural networks, a new-age branch of machine learning, have emerged as a powerful weapon for better forecasting in the stock market. Neural networks, especially deep learning models such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), are most suited to learning complex patterns from the historical data and reveal hidden correlations or relationships between different variables which have not been captured by the traditional models. The article discusses the role of neural networks in predicting stock market trends and the applications of these models in financial forecasting. The fundamentals of neural networks are explored, their special applications in stock market prediction, and the advantages they confer over traditional techniques. The objective of this article is to also provide insights into key obstacles, including data quality, overfitting, and interpretability, which can impinge upon the effective use of neural networks in finance. Case studies and simulated situations help present neural networks' use, where they are positioned to enhance stock market prediction output with accuracy and reliability. It seems that future directions that research in this field will take would involve integration with different alternative data sources, with quantum computing anticipated to change the landscape of financial prediction models. The insights provided... Keywords: Neural Networks, Stock Market Predictions, Machine Learning, Financial Forecasting, Trend Analysis, Deep Learning, Predictive Analytics, Data Sciences, and Algorithmic Trading.

Now knowledge pre-processing, model and reasoning issues, power metrics, quality issues, post-processing of discovered structures, visualization, and on-line change is best challenge. In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the markets(9). The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform intelligent tasks similar to those performed by the human brain. Artificial Neural Networks are being counted as the wave of the future in computing. They are indeed self-learning mechanisms which don't require the traditional skills of a programmer. Back propagation is one of the approaches to implement concept of neural networks. Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most often used as training algorithm in current neural network applications. In this paper, we apply data mining technology to stock market in order to research the trend of price; it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast accuracy.

The article presents the issue of monitoring the sociopsychological state of society in the period of epidemics by means of neural network algorithms and methods. Publications of sociologists, psychologists, and philologists, who have created a number of methods for in-depth analysis of emotions and tonality of texts in the Internet media, including cognitive and interpretive decoding, are devoted to substantiating approaches and methods for studying the content of Internet content. The purpose of the study is to substantiate the methods and computer tools for studying the socio-psychological state of society in crisis situations, in particular epidemics, based on neural network technologies based on Internet resources. The article considers methodological approaches and particular methods of their computer implementation. It is shown that for the computer analysis of the psychological state of society in the context of the epidemic, it is necessary to adapt the methodology for designing neural network technologies, as well as systems for collecting and textual analysis of the content of electronic and Internet resources. An effective approach to creating such systems is embedding, which uses a dense vector representation of tokens in a multidimensional space, the dimension of which should be selected experimentally in the process of training and testing the developed artificial neural networks (ANN). For contextual neural network analysis, a multiclass-oriented ANN with regularization layers of the form “SpatialDropout1D”can be used. The neural network architecture can be built on fully connected layers with an activation function of the “ReLU” type. The scientific and applied significance of the results of neural network analysis based on Internet resources is the possibility of obtaining classified assessments and segmentation of target information about the psychological state of society during periods of epidemics. This information can be used to effectively counter information threats to society.

Basketball is one of the popular sports in colleges. Basketball injuries are a common thing, and the use of machine learning and other technologies can effectively reduce basketball injuries, which should start with prevention. Nonstandard basketball movements and lack of physical coordination will not only reduce sports efficiency for athletes but also increase the probability of injury. Therefore, effective reduction and targeted prevention of nonstandard actions are of great significance to college basketball. With the development of science and technology, artificial intelligence technology is closer to our lives. Based on the machine learning platform, this paper studies basketball injuries from the perspective of the integration of sports and medicine. Research on what aspects cause college students' basketball injuries is needed for the future. Effectively preventing college students from being injured in basketball is an urgent problem in the field of sports medicine. To find the most suitable machine learning platform for college basketball injury research, this article will introduce three different methods for comparative analysis. The techniques used in the experiment in this paper are traditional BP neural network technology, SCG neural network technology, and RBF neural network technology. Through experiments, it is known that, through experiments, RBF neural network technical prediction accuracy rate is as high as 95.4%, which is a relatively good neural network algorithm for studying the basketball loss of college students.

Introduction . Approximately 20 % of reproductive age women are obese, and more than half of them experience menstrual irregularities, anovulation, and infertility. Aim: to determine predictors of ovulation restoration and to develop a model for individual treatment selection in patients with obesity and oligo-/amenorrhea based on neural network technology. Materials and Methods . The prospective randomized controlled study included 80 women – patients with obesity and oligo-/amenorrhea, divided into 2 groups, who received the following therapy for 6 months: 40 patients (group I) – a combination of myoinositol, D-chiroinositol, folic acid and manganese, the other 40 patients (group II) – metformin. After treatment, patients from both groups were divided into two clusters based on the "anovulation/ovulation" criterion. Anthropometric parameters were determined, pelvic organs ultrasound was performed, laboratory tests (indicators of carbohydrate and fat metabolism, amino acids and peptides, hormonal status, blood cytokine levels, inflammation markers, and blood micronutrient composition) were performed. To create a model for predicting ovulation restoration, a multilayer perceptron procedure was used. The diagnostic value of the prognostic model was determined using ROC analysis. Results . The average age of the patients was 27.9 ± 3.8 years, with the average body mass index (BMI) 33,4 [31.2; 34.0] kg/m2. The following parameters were identified as significant anovulation predictors using neural network analysis: waist-to-hip ratio (WHR), menstrual cycle duration, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), C-reactive protein, leptin, follicle-stimulating hormone, 25-hydroxycalciferol (vitamin D), and tumour necrosis factor alpha. Because these parameters reflect the metabolic profile in patients, interventions to restore ovulation should primarily be aimed at correcting it. The developed model for predicting the onset of ovulation has a sensitivity of 100 %, specificity of 80 %, accuracy of 93.8 %; the area under the ROC curve is 0.985 (p < 0.001), which allows to consider it sufficiently informative for specific drug selection. For practical purposes, an online calculator for individual drug selection has been developed (accuracy – 91.7 %). Conclusion. An integrated approach based on neural network analysis of study parameters available for wide clinical practice is promising for predicting the onset of ovulation while using a specific drug due to its high information content.

Neural networks are a popular area of research today. However, neural network algorithms have only recently proven valuable to application problems. This paper seeks to aid in the process of transferring neural network technology from research to a development environment by describing our experience in applying this technology.

Many advanced methods proposed for control of robot manipulators are based on the dynamic models of the robot systems. Model-based control design needs a correct dynamic model and precise parameters of the system. Practically speaking, however, every dynamic model has some degrees of incorrectness and every parameter associates with some degrees of identification error. The incorrectness and errors eventually result positioning or trajectory tracking errors, and even cause the system to be unstable. In the past two decades, intensive research activities have been devoted on the design of robust control systems and adaptive control systems for the robot in order to overcome the control system drawback caused by the model errors and uncertain parameters, and a great number of research results have been reported, for example, (Hsia, 1989), (Kou, and Wang, 1989), (Slotine and Li, 1989), ( Spong, 1992), and (Cheah, Liu and Slotine, 2006). However, almost parts of results associate with complicated control system design approaches and difficulties in the control system implementation for industrial robot manipulators. Recently, neural network technology attracts many attentions in the design of robot controllers. It has been pointed out that multi-layered neural network can be used for the approximation of any nonlinear function. Other advantages of the neural networks often cited are parallel distributed structure, and learning ability. They make such the artificial intelligent technology attractive not only in the application areas such as pattern recognition, information and graphics processing, but also in intelligent control of nonlinear and complicated systems such as robot manipulators (Sanger, 1994), (Kim and Lewis, 1999), (Kwan and Lewis, 2000), (Jung and Yim, 2001) (Yu and Wang, 2001). A new field in robot control using neural network technology is beginning to emerge to deal with the issues related to the dynamics in the robot control design. A neural network based dynamics compensation method has been proposed for trajectory control of a robot system (Jung and Hsia, 1996). A combined approach of neural network and sliding mode technology for both feedback linearization and control error compensation has been presented (Barambones and Etxebarria, 2002). Sensitivity of a neural network performance to learning rate in robot control has been investigated (Clark and Mills, 2000). In the following, we present a simple control system consisting of a traditional controller and a neural network controller with parallel structure for trajectory tracking control of 16