Uncertainty Of Initial Data Research Articles

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Method for solving distributional problems of mathematical programming under conditions of fuzzy initial data

The object of this study is a large class of mathematical programming problems under conditions of uncertainty of initial data. The formulated object generates a subclass of problems of rational distribution of a limited resource under conditions of initial data described in terms of fuzzy mathematics. The conventional, standardly used method for solving such problems is based on optimization on average. To obtain such a solution, it is sufficient to replace all fuzzy initial data with their modal values in the analytical description of the mathematical model of the corresponding problem. To solve the resulting deterministic problem, one can use known methods of mathematical programming. However, the results of such a solution can be used in practice if the carriers of fuzzy parameters are specified compactly, that is, the intervals of possible values of fuzzy parameters of the problem are small. Otherwise, the implementation of this solution may lead to unpredictably large losses. Other alternative approaches are based on the use of insufficiently informative estimates of the best or worst possible values of fuzzy parameters of the problem. These circumstances make the statement of the problem and the objective of the study relevant: devising a method for solving the problem of rational distribution of a limited resource under conditions of fuzzy initial data. To solve the stated problem of rational distribution of a limited resource, a productive idea of constructing the proposed general optimization method under conditions of uncertainty of the initial data has been constructively implemented. In this case, the initial problem is reduced to a clear problem of optimizing a complex criterion constructed on the basis of the objective function of the initial problem and a set of membership functions of its fuzzy parameters. An example of solving the problem has been considered, leading to a solution that is better than that obtained on the basis of the modal values of the fuzzy parameters of the problem

Game Analysis of Strategies for Providing Material Support to the Population

The article focuses on the possibilities of game analysis of options for providing material support to the population, implemented in the form of a game with nature – a generalized player, whose inclusion in the game analysis allows us to consider the diverse interaction of economic agents with the socio-economic environment. This set of methods has a number of advantages among the complex of mathematical methods in economics and management in conditions when regression models become irrelevant due to increasing uncertainty or lack of sufficient initial data. The purpose of the study is to overcome the insufficient use of the potential of game models in the practice of modeling social situations, taking into account the real information conditions that have developed to date. The research methods are the methods of game theory (construction of a set of active strategies for providing material support to the population; identification of possible variants of states of nature reflecting the possibilities of the number of people who really need material support; determination of the optimal strategy for providing material support, taking into account the selected optimality criterion). Their use contributes to improving the quality of decisions made in the field of providing material assistance to the population. Among the results of the study, we will indicate the implementation of all the necessary stages of the game analysis of strategies for providing material support to the population. The proposed mechanism for assessing the usefulness of funds, used for material support of the population, takes into account various contributions to the assessment of the final utility of the distribution of funds, arising both in cases of receiving funds by those in need and in cases of non-receipt of funds by those in need, as well as in cases of receiving funds by citizens who do not really need financial assistance. The authors have made calculations and assessed the consequences of strategies, the implementation of which involves reducing the number of citizens who receive financial assistance (with simultaneous dynamics of the amount of financial assistance). The game analysis of strategies for providing material support to the population became possible due to the construction of a game model of a basic level of complexity that takes into account the main scenarios of the development of the social situation under consideration. In the process of studying the constructed game model, a high degree of sensitivity of strategies for providing material assistance to the population to the choice of the optimality criterion, as well as its parameter, was established. This feature requires clarification of the information situation in which the strategy of providing financial assistance is chosen. In conclusion, we note that the material of the article may be useful for consideration with subsequent pilot testing at various levels of state and municipal administration, when developing and adopting legislative initiatives on social support measures aimed at strengthening the targeting of financial assistance to the population. Also, the material and tools of this study can serve in the course of improving existing and developing new academic disciplines, the content of which is related to the quantitative analysis of socio-economic problems and situations.

Nuclear data uncertainty on generation IV fast reactors criticality calculations analysis comparison

The new calculation code capabilities are applied in the current work as well as important fast reactor criticality parameters uncertainty assessment articles’ results based on different nuclear data libraries and covariance matrices. A comparative analysis of uncertainty estimations related to neutron reactions is presented for lead-cooled reactor models and sodium-cooled reactor models. For the models of advanced BN and BR fast reactors with three fuel types (UO2, MOX, MNUP), the multiplication factor uncertainty calculations are performed using 252-group covariance matrices based on ENDF/B-VII.1 library via the SCALE 6.2.4 code system. The main nuclear data uncertainty contributors in the multiplication factor are determined. Recommendations are formulated for improving the cross sections accuracy for several nuclides in order to provide more reliable results of fast reactor criticality calculations. Lead-cooled reactors have no operational history compared to light-water and sodium-cooled reactors. The experimental data insufficiency calls in the question about reliability of the simulation results and requires a comprehensive initial data uncertainty analysis for the neutron transport simulation. The obtained results support the idea that lead- and sodium-cooled reactors have close nuclear data sensitivity using one and the same computation tools, nuclear data libraries and fuel compositions. This makes it possible to use the accumulated data of benchmarks for sodium-cooled reactors in the safety determination of lead-cooled reactors.

Exploring the superiority of human expertise over algorithmic expertise in the cognitive and metacognitive processes of decision-making among decision-makers.

Investigating the role of human vs algorithmic expertise on decision-making processes is crucial, especially in the public sector where it can impact millions of people. To better comprehend the underlying cognitive and metacognitive processes, we conducted an experiment to manipulate the influence of human and algorithmic agents on decision-makers' confidence levels. We also studied the resulting impact on decision outcomes and metacognitive awareness. By exploring a theoretical model of serial and interaction effects, we were able to manipulate the complexity and uncertainty of initial data and analyze the role of confidence in decision-making facing human or algorithmic expertise. Results showed that individuals tend to be more confident in their decision-making and less likely to revise their decisions when presented with consistent information. External expertise, whether from an expert or algorithmic analysis, can significantly impact decision outcomes, depending on whether it confirms or contradicts the initial decision. Also, human expertise proved to have a higher impact on decision outcomes than algorithmic expertise, which may demonstrate confirmation bias and other social processes that we further discuss. In conclusion, the study highlights the importance of adopting a holistic perspective in complex decision-making situations. Decision-makers must recognize their biases and the influence of external factors on their confidence and accountability.

Development of a method for the search of solutions in the sphere of national security using bio-inspired algorithms

The object of research are decision support systems. The subject of research is the decision-making process in management problems using bio-inspired algorithms. A method for the search of solutions in the field of national security using bio-inspired algorithms is proposed. The proposed method is based on a combination of an artificial bat algorithm and evolving artificial neural networks. The method has the following sequence of actions: ‒ input of initial data; ‒ processing of initial data taking into account the degree of uncertainty; ‒ numbering of bat agents (BA); ‒ placement of bat agents taking into account the degree of uncertainty about the state of the analysis object in the search space; ‒ setting the initial BA speed and the echolocation frequency of each BA; ‒ starting a local search; ‒ launching a global search; ‒ training knowledge bases of bat agents. The originality of the proposed method consists in the arrangement of bat agents taking into account the uncertainty of initial data, improved global and local search procedures taking into account the noise degree of data about the state of the analysis object. Another feature of the proposed method is the use of an improved procedure for training bat agents. The training procedure consists in learning the synaptic weights of an artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 13–21 % due to the use of additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic processes in the interests of solving national security problems

Study of the Sensitivity of the Calculated Radiation Load on VVER-1000 Internals to the Uncertainty of Initial Data

Study of the Sensitivity of the Calculated Radiation Load on VVER-1000 Internals to the Uncertainty of Initial Data

Importance analysis of decision making factors based on fuzzy decision trees

Classification is one important and commonly used Machine Learning technique. The classification effectiveness can be influenced by different factors and initial data used for the classifier induction and in the process of classification, including. The quality of measurements of the input attributes of new samples for the classification can affect the accuracy and reliability of the classification result. At the same time, the degree of influence of different attributes is not the same. There are some attributes that are most important for the classification because have a greater influence on the classification result than others. A new method for the determination of the most important attributes is proposed. This method is developed based on the approach of Importance Analysis, which is widely used in reliability engineering. The proposed method investigates the sensitivity of the classification from the input attributes and indicates the attributes for which changes have the most impact on the classification result. The attribute’s importance is evaluated by the special index which is known as structural importance in reliability engineering. This method application is illustrated in the paper by Fuzzy Decision Tree, but it can be used for any other classifiers induction. The use of a fuzzy classifier allows for taking into consideration of possible uncertainty of initial data.

A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty

The development of the system's model is an important step in reliability analysis. The system's model in reliability analysis similar to other knowledge areas is used as an explanatory and research tool. In reliability analysis, this model is mathematical and allows us to determine quantitative characteristics to evaluate the behavior of the original system. Typically such a mathematical model approximates the behavior of the initial system and has some uncertainty. However, this uncertainty can increase significantly if the initial data for building the model is uncertain and incompletely specified. The uncertainty of the mathematical model also causes incorrect estimates of the system behavior and its reliability. Therefore, it is important to develop methods that allow us to take into account the uncertain nature of the initial data, first of all, take into account epistemic uncertainty in initial data. A new method for the development of a mathematical model of a Multi-State System (MSS) in the form of a Multi-Valued Decision Diagram based on incompletely specified and uncertain data is proposed. In other words, the proposed method takes into account the epistemic uncertainty of the initial data. The specific of this method is the use of Data Mining based classification procedures, in particular, the Fuzzy Decision Trees.

Analysis of semi-Markov systems with fuzzy initial data

In real operating conditions of complex systems, random changes in their possible states occur in the course of their operation. The traditional approach to describing such systems uses Markov models. However, the real non-deterministic mechanism that controls the duration of the system's stay in each of its possible states predetermines the insufficient adequacy of the models obtained in this case. This circumstance makes it expedient to consider models that are more general than Markov ones. In addition, when choosing such models, one should take into account the fundamental often manifested feature of the statistical material actually used in the processing of an array of observations, their small sample. All this, taken together, makes it relevant to study the possibility of developing less demanding, tolerant models of the behavior of complex systems. A method for the analysis of systems described under conditions of initial data uncertainty by semi-Markov models is proposed. The main approaches to the description of this uncertainty are considered: probabilistic, fuzzy, and bi-fuzzy. A procedure has been developed for determining the membership functions of fuzzy numbers based on the results of real data processing. Next, the following tasks are solved sequentially. First, the vector of stationary state probabilities of the Markov chain embedded in the semi-Markov process is found. Then, a set of expected values for the duration of the system's stay in each state before leaving it is determined, after which the required probability distribution of the system states is calculated. The proposed method has been developed to solve the problem in the case when the parameters of the membership functions of fuzzy initial data cannot be clearly estimated under conditions of a small sample

Study of the Fractional-Order HIV-1 Infection Model with Uncertainty in Initial Data

Uncertainty always lives with us. We cannot take exact measurement of initial conditions or parameters values in a mathematical model. As humans, we are remaining alive in an environment where the uncertainties lie in the modelling of physical phenomena. There might be some incomplete information or estimation of the parameter or initial values. To handle uncertainty, we use fuzzy operators rather than classical operators. In this paper, we study a model of HIV-1 infection by taking uncertainty in the initial data under Caputo fractional operator. We explore the existence and uniqueness of the results through fixed-point theory. We study the Ulam–Hyres stability of the considered model. By using the fuzzy Laplace Adomian decomposition method, numerical results are obtained for specific fuzzy initial conditions. To better understand the behaviour of the fuzzy solution, we present the obtained numerical results graphically for various fractional orders where the uncertainty lies in [0, 1].

Short-Term Planning for the Operation of an Industrial Power Plant Taking Into Account Daily Fluctuations in the Wholesale Price of Electricity and the Uncertainty of Initial Data

Short-Term Planning for the Operation of an Industrial Power Plant Taking Into Account Daily Fluctuations in the Wholesale Price of Electricity and the Uncertainty of Initial Data

An information technique for segmentation of military assets in conditions of uncertainty of initial data

To solve the applied task of detecting military assets in aerospace images the presented paper investigates the processes of constructing segmented maps of the images. The goal is to develop an information technique for detecting military assets in conditions of uncertainty of initial data. To achieve the goal, the following tasks were formulated: 1) to analyze usability of the existing segmentation methods for automatic detection of military assets in the images; 2) if the existing methods are inapplicable, to develop a new algorithm to solve the problem. In the paper the following methods are used: the methods of digital image processing, the methods of Boolean algebra and fuzzy sets, the methods of statistical analysis. The following results are received. Analysis of the known segmentation methods showed that due to camouflage coloring of the military assets, similarity of their color characteristics to those of underlying surfaces and due to the presence of large number of textured fragments in the images those methods provide segmented maps of poor quality. Among the common problems arising when conventional methods are used there are wrong segmentation, when the received contours do not coincide with the borders of the objects of interest; oversegmentation, when there are a lot of minor segments which produce "litter" objects; undersegmentation, when potentially possible segments are missed etc. As the conventional methods are inapplicable, in the paper it is suggested using the fuzzy logic systems. For each pixel the probability of the fact that the pixel belongs to the object or to the background is calculated. For making decision whether a pixel belongs to the object the production rules based on the chosen most significant factors (probabilistic values of spectral sub-bands, belonging of the neighboring pixels to the object, jumps of brightness in spectral sub-bands on the object's borders) are constructed. Conclusion. The suggested technique ensures high-quality definition of objects' borders, thus considerably increasing the reliability of military assets recognition.

Fuzzy Decision Tree Based Method in Decision-Making of COVID-19 Patients’ Treatment

A new method in decision-making of timing of tracheostomy in COVID-19 patients is developed and discussed in this paper. Tracheostomy is performed in critically ill coronavirus disease (COVID-19) patients. The timing of tracheostomy is important for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. The analysis of this timing has been implemented based on classification method. One of principal conditions for the developed classifiers in decision-making of timing of tracheostomy in COVID-19 patients was a good interpretation of result. Therefore, the proposed classifiers have been developed as decision tree based because these classifiers have very good interpretability of result. The possible uncertainty of initial data has been considered by the application of fuzzy classifiers. Two fuzzy classifiers as Fuzzy Decision Tree (FDT) and Fuzzy Random Forest (FRF) have been developed for the decision-making in tracheostomy timing. The evaluation of proposed classifiers and their comparison with other show the efficiency of the proposed classifiers. FDT has best characteristics in comparison with other classifiers.

Development of object state estimation method in intelligent decision support systems

A method of object state estimation in intelligent decision support systems (DSS) has been developed. The essence of the method is to ensure a high-quality analysis of the current state of the analyzed object. The key difference of the developed method is the use of an advanced genetic algorithm. The advanced genetic algorithm is used when constructing a fuzzy cognitive model and increases the efficiency of identifying factors and relationships between them by simultaneously finding a solution by several individuals. The objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The method also contains an improved procedure for processing initial data under a priori uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method increases the efficiency of data processing at the level of 11–15 % using additional advanced procedures. The proposed method can be used in DSS of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, as well as in DSS for logistics ACS of the Armed Forces

Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PDF

About stability of mixed integer optimization problems under perturbations of input data of vector criterion

The article is devoted to the study of the influence of uncertainty in initial data on the solutions of mixed integer optimization vector problems. In the optimization problems, including problems with vector criterion, small perturbations in initial data can result in solutions strongly different from the true ones. The problem of stability of the indicated tasks is studied from the point of view of direct coupled with her question in relation to stability of solutions belonging to some subsets of feasible set.

Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine

SELECTION OF THE OPTIMUM ROUTE IN AN EXTENDED TRANSPORTATION NETWORK UNDER UNCERTAINTY

Relevance. For a given values set of extensive transport network sections lengths an exact method has been developed for finding optimal routes. The method provides an approximate solution when the initial data - are random variables with known distribution laws, as well as if these data are not clearly specified. Fora special case with a normal distribution of the numerical characteristics of the network, solution is brought to the final results. Method. An exact method of deterministic routing is proposed, which gives an approximate solution in case of random initial data. The method is extended to the case when the initial data are described in theory of fuzzy sets terms. The problem of stability assessing of solutions to problems of control the theory under conditions of uncertainty of initial data is considered. Results. A method of optimal routes finding is proposed when the initial data are deterministic or random variables with known distribution densities. A particular case of a probabilistic - theoretical description of the initial data is considered when can be obtained a simple solution of problem. Proposed method for obtaining an approximate solution in the general case for arbitrary distribution densities of random initial data. The situation is common when the initial data are not clearly defined. A simple computational procedure proposed for obtaining a solution. A method for stability assessing of solutions to control problems adopted under conditions of uncertainty in the initial data, is considered.

FUZZY ROBUST ESTIMATES OF LOCATION AND SCALE PARAMETERS OF A FUZZY RANDOM VARIABLE

A random variable is a variable whose components are random values. To characterise a random variable, the arithmetic mean is widely used as an estimate of the location parameter, and variation as an estimate of the scale parameter. The disadvantage of the arithmetic mean is that it is sensitive to extreme values, outliers in the data. Due to that, to characterise random variables, robust estimates of the location and scale parameters are widely used: the median and median absolute deviation from the median. In real situations, the components of a random variable cannot always be estimated in a deterministic way. One way to model the initial data uncertainty is to use fuzzy estimates of the components of a random variable. Such variables are called fuzzy random variables. In this paper, we examine fuzzy robust estimates of location and scale parameters of a fuzzy random variable: fuzzy median and fuzzy median of the deviations of fuzzy component values from the fuzzy median.