Use Of Fuzzy Model Research Articles

IMPLEMENTATION OF INTELLIGENT AUTOMATIC CONTROL OF TRAFFIC FLOWS IN URBAN AREAS OF REGULATION BASED ON THE USE OF FUZZY MODELS

The article deals with the implementation of automatic traffic control of automobile flows in urban areas of regulation based on the use of fuzzy models. The relevance of the topic of the article is due to the problem of traffic management in the Smart City ecosystem. Traffic flow control is a complex dynamic task, for the solution of which it is proposed to use artificial intelligence methods for processing fuzzy knowledge. The article proposes a model of a traffic flow control system at an intersection based on the use of fuzzy knowledge. Knowledge processing in the system is carried out by the module “Fuzzy Controller”. The input data for the fuzzy controller is information about the number of cars that have passed and information about the current duration of the traffic light phases. The fuzzy controller has a number of output variables corresponding to the number of phases of the traffic light. The fuzzy controller is implemented by means of the fuzzy sets apparatus. The system solves the following tasks: tracking the increase in traffic in the regulation zone; tracking the approach of the flow density on all streets of the regulation zone to the critical one; collecting information about the filling of the road departing from the intersection; implementing indirect unloading of the road section after the intersection, implementing management in transit sections of the city. To increase the efficiency of the model, an improved traffic management process is proposed within the framework of a single intersection, which takes into account traffic situations after the intersection. This approach has a positive impact on traffic in the regulated area due to the decentralized structure of the system consisting of such controlled intersections. The authors also implement the priorities of the directions of movement within the framework of the proposed model. Priorities are set when setting up the system at each of the traffic lights and allow you to speed up the circulation of traffic within the control zone.

A Fuzzy Modelling of a Hybrid MCDM Method for Supplier Selection

This article examines the significance of supplier selection in the procurement process, which has grown in prominence as a result of globalization and outsourcing. When selecting the best suppliers, supply chain managers must consider a variety of quantitative and qualitative aspects, as these have a substantial impact on supply chain performance. Multi-criteria decision-making (MCDM) approaches can help in this process by taking into account many competing considerations. However, due to uncertainties and ambiguity, supplier selection is a complex process, and fuzzy multi-criteria decisionmaking approaches can be used to determine the best supplier for the company's key operations. This research suggests a hybrid MCDM strategy that makes use of fuzzy modeling to help with complex decision-making processes. Organizations can improve their supply chain performance by selecting the best supplier based on numerous parameters such as cost, quality, delivery time, and supplier reputation.

RISK ASSESSMENT MODELING OF ERP-SYSTEMS

Context. Because assessing security risks is a complex and complete uncertainty process, and uncertainties are a major factor influencing valuation performance, it is advisable to use fuzzy methods and models that are adaptive to noncomputed data. The formation of vague assessments of risk factors is subjective, and risk assessment depends on the practical results obtained in the process of processing the risks of threats that have already arisen during the functioning of the organization and experience of security professionals. Therefore, it will be advisable to use models that can ade-quately assess fuzzy factors and have the ability to adjust their impact on risk assessment. The greatest performance indicators for solving such problems are neuro-fuzzy models that combine methods of fuzzy logic and artificial neural networks and systems, i.e. “human-like” style of considerations of fuzzy systems with training and simulation of mental phenomena of neural networks. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models (Rule-Based Fuzzy Models/Systems) this is a common type of fuzzy models used to describe, analyze and simulate complex systems and processes that are poorly formalized.
 Objective. Development of a fuzzy model of quality of security risk assessment and protection of ERP systems through the use of fuzzy neural models.
 Method. To build a model for calculating the risk assessment of security, it is proposed to use a fuzzy product model. Fuzzy product models are a common kind of fuzzy models used to describe, analyze and model complex systems and processes that are poorly formalized.
 Results. Identified factors influencing risk assessment suggest the use of linguistic variables to describe them and use fuzzy variables to assess their qualities, as well as a system of qualitative assessments. The choice of parameters was substantiated and a fuzzy product model of risk assessment and a database of rules of fuzzy logical conclusion using the MATLAB application package and the Fuzzy Logic Toolbox extension package was implemented, as well as improved by introducing the adaptability of the model to experimental data by introducing neuro-fuzzy components into the model. The use of fuzzy models to solve the problems of security risk assessment, as well as the concept and construction of ERP systems and the analyzed problems of their security and vulnerabilities are considered.
 Conclusions. A fuzzy model has been developed risk assessment of the ERP system. Selected a list of factors affecting the risk of security. Methods of risk assessment of information resources and ERP-systems in general, assessment of financial losses from the implementation of threats, determination of the type of risk according to its assessment for the formation of recommendations on their processing in order to maintain the level of protection of the ERP-system are proposed. The list of linguistic variables of the model is defined. The structure of the database of fuzzy product rules – MISO-structure is chosen. The structure of the fuzzy model was built. Fuzzy variable models have been identified.

DEVELOPING A FUZZY RISK ASSESSMENT MODEL FOR ERPSYSTEMS

Context. Because assessing information security risks is a complex and complete uncertainty process, and uncer-tainties are a major factor influencing valuation performance, it is advisable to use fuzzy methods and models that are adaptive to non-calculated data. The formation of vague assessments of risk factors is subjective, and risk assessment depends on the practical results obtained in the process of processing the risks of threats that have already arisen during the functioning of the organization and experience of information security professionals. Therefore, it will be advisable to use models that can adequately assess fuzzy factors and have the ability to adjust their impact on risk assessment. The greatest performance indicators for solving such problems are neuro-fuzzy models that combine methods of fuzzy logic and artificial neural networks and systems, i.e. “human-like” style of considerations of fuzzy systems with training and simulation of mental phenomena of neural networks. To build a model for calculating the risk assessment of information security, it is proposed to use a fuzzy product model. Fuzzy product models (Rule-Based Fuzzy Models/Systems) this is a common type of fuzzy models used to describe, analyze and simulate complex systems and processes that are poorly formalized.
 Objective. Development of the structure of a fuzzy model of quality of information security risk assessment and protection of ERP systems through the use of fuzzy neural models.
 Method. To build a model for calculating the risk assessment of information security, it is proposed to use a fuzzy product model. Fuzzy product models are a common kind of fuzzy models used to describe, analyze and model complex systems and processes that are poorly formalized.
 Results. Identified factors influencing risk assessment suggest the use of linguistic variables to describe them and use fuzzy variables to assess their qualities, as well as a system of qualitative assessments. The choice of parameters is substantiated and the structure of the fuzzy product model of risk assessment and the basis of the rules of fuzzy logical conclusion is developed. The use of fuzzy models for solving problems of information security risk assessment, as well as the concept and construction of ERP systems and analyzed problems of their security and vulnerabilities are considered.
 Conclusions. A fuzzy model has been developed risk assessment of the ERP system. Selected a list of factors affecting the risk of information security. Methods of risk assessment of information resources and ERP-systems in general, assessment of financial losses from the implementation of threats, determination of the type of risk according to its assessment for the formation of recommendations on their processing in order to maintain the level of protection of the ERP-system are proposed. The list of linguistic variables of the model is defined. The structure of the database of fuzzy product rules – MISO-structure is chosen. The structure of the fuzzy model was built. Fuzzy variable models have been identified.

Application of Fuzzy Logic to Assess Banks' Credit Risk

The banking system is a constantly evolving system. The information environment of the bank is growing, the volumes of processed information are increasing due to the growth of users and banking products. To reduce risks, banks make a financial assessment of the situation of individuals and legal entities. The aim of the work is to develop fuzzy multi-connected models designed to predict the receipt of a positive or negative decision to receive a banking product. The decision is made based on scoring. Scoring consists in assigning points for completing a certain questionnaire developed by underwriters of credit risk assessors. Based on the results of the points gained, the system automatically makes a decision on approving or refusing to issue a loan. Different banks have diffe¬rent scoring models. Purpose of the study. The paper considers the use of fuzzy models for making a decision by a bank to issue a banking product that implements the concept of “soft computing”. Methods. The use of fuzzy logic methods in credit scoring is not new, but it is not widely used in practice because it is expensive to integrate into existing systems. Each bank uses its own indicators of the client's financial reliability in scoring. Most of the indicators in banks are the same, but when deciding to issue different banking products, they have different numerical values. The data of the standard scoring methodology of a real bank were taken as the initial data. To predict a bank's decision to issue a banking product to a client, a fuzzy model was applied, production rules were proposed, and membership functions were determined. The model focused on the simultaneous processing of incoming data from multiple clients and for different banks and different scoring models. Results. The developed mathematical model for assessing the client's rating and predicting the decision to receive a banking product based on the fuzzy inference rule. The obtained results are proposed to be used in a multi-banking web-oriented system of providing banking products to corporate clients.

Innovation Management in the Supply Chains of Russian Natural Gas, Taking into Account the Regional Structure of its Production

The article describes the role of Russia in the global gas industry, on the basis of the analysis of the factual data cited, conclusions are drawn about the provision of the economy with the considered minerals, the main problems in managing the supply chains of natural gas and liquefied natural gas are formulated, and innovative ways to solve them are proposed. Based on the fuzzy modeling method, it was shown that the maximum amount of budget revenues from the export duty of Russian natural gas from the main gas-bearing regions can almost double. At the same time, the minimum volume in the same case remains virtually unchanged, remaining within 10%. The use of fuzzy modeling in innovation management in the supply chains of Russian natural gas is caused by high volatility in the global commodity markets, both for the underlying asset (in this case, natural gas) and for derivative financial instruments (derivatives). Based on the simplex optimization method, innovation management schemes have been developed in the supply chains of Russian natural gas, taking into account the regional structure of its production. The differences in the economic efficiency of the export duty for the Russian budget between the supply of pipeline natural gas and liquefied natural gas are shown. The author’s methods presented in the article can be used as additional innovative tools for making strategic and operational management decisions by industry leaders and public authorities.

Software test quality evaluation based on fuzzy mathematics

The thesis starts with the connotation and attributes of software testing quality, introduces software testing quality evaluation methods, and analyzes and discusses software testing quality evaluation models based on fuzzy mathematics theory. Focusing on the key technical problems of software testing quality, discuss the key technologies to solve the software testing quality evaluation model establishment. Through the use of fuzzy models, the cost of software testing quality evaluation is effectively reduced, and the reliability of software testing quality evaluation methods is improved. This model can quickly evaluate the quality of software testing, can avoid the occurrence of local maxima, overcome the shortcomings of existing evaluation models and tools, and can correctly reflect the relationship between the internal and external properties of the software. Using the new software testing quality evaluation method, comparing the evaluation models and tools used before, summarizing the methods of software testing quality improvement. The application of these methods effectively improves the software testing quality.

CONFORTO TÉRMICO EM ABRIGO INDIVIDUAL PARA BEZERROS COM FORRO NÃO CONVENCIONAL DE SUBPRODUTOS AGROINDUSTRIAIS

This study evaluated the thermal comfort conditions in two individuals calve shelter models covered with fiber cement tile without ceiling and using a ceiling made of low density particleboards from agro-industrial by-products (40% sugarcane bagasse and 60% Pinus sp. shavings) bonded with polyurethane bi-component resin based on castor oil. In the ambience study, dry bulb temperature (DBT), black globe temperature (BGT) and relative humidity (RH) of the calf hutches were evaluated. Indices, such as Black Globe Temperature and Humidity Index (BGHI) and Radiant Thermal Load (RTL), and also thermographic images of the internal surface of the shelters were studied. The use of fuzzy modeling has been proposed, allowing the comparison of the BGT, BGHI and RTL indices, and via these values, attributing the comfort situation, thus seeking to optimize the experimental evaluations. The results indicate that the presence of the ceiling did not contribute to a heat level reduction inside the shelters, which presented above the comfort conditions for the animals during the summer. Through observation, there was a reduction of the internal temperature of the surfaces of the shelter with a ceiling.

Fuzzy-based methodological proposal for participatory diagnosis in the linear parks management

Participatory decision-making in the management of linear parks can provide better outcomes than classical top-down procedures. However, the integration of technical knowledge with the understanding of the community requires an approach able to deal with uncertainty, due to the issues of vagueness and subjectivity. Therefore, the present paper introduces a fuzzy-based proposal for supporting participatory diagnosis. For this purpose, five Mamdani-type systems were built based on the knowledge of experts, including as indicators the species richness of fauna and flora, land cover, basic and recreational facilities, support services, and the park users’ perceptions. By integrating such indicators, three indexes were obtained for assessing the natural condition, infrastructure, and functional performance of a linear park. The proposed system was then applied in a case study of the Tiquatira linear park in São Paulo city, Brazil. The results indicated that the park has high functional performance, but that improvements in its natural condition and infrastructure are needed, such as the reclamation of disturbed areas and ongoing maintenance of the facilities. The use of fuzzy modeling enabled integration of the technical assessment by experts and the users’ perception, in order to support a participatory diagnosis taking into account the existing uncertainties. In conclusion, this fuzzy-based proposal can be considered a promising approach for the participatory management of linear parks. In future studies, techniques for the case-by-case weighting of variables could be assessed, in order to strengthen the systems described in the present paper.

Проектування нечітких моделей інтелектуальних промислових регуляторів і систем управління

The problems of the design of industrial controllers and control systems with varying degrees of intelligence. Noted shortcomings of existing software and hardware tools to support management processes using the knowledge and methods of intelligent control systems. Emphasizes the need to develop a special methodology of designing control systems in its class. Provides a framework of multi-level design methodology of intelligent automatic control systems (ACS). Analyzes the characteristics of intelligent control systems.The use of fuzzy models and regulators, in particular control systems "intellectual in the large" can significantly improve the dynamic characteristics of the Shelter functioning in the conditions of uncertainty of input information or chaotically organized environment. Designing control systems of this class is a complex issue, since obtaining the required dynamic performance achieved in different ways: either by the complexity of the model executive level or through the involvement of intelligent tools with more features. The proposed methodology of the article makes it possible to set the desired correspondence between the executive and the intellectual level of the results of the simulation, depending on the orientation of the problem the problem, management objectives and modalities of the op amp.The basis of the design methodology of control systems with varying degrees (levels) of the intellectual component of simulation methods, learning and adaptation using neural networks, allowing to form a special knowledge base received intellectual level. This makes it possible to formulate the basic requirements for the respective CAD system, the base of which consists of such blocks as block simulation, fuzzy neural network learning and adaptation mechanisms of the block approximate reasoning (including fuzzy inference), driver knowledge base, driver and interpreter fuzzy operations.

Assessment of Fence Systems Usiing Fuzzy Modeliing

The article deals with the use of fuzzy modeling for time estimate of wire fence systems resistance. The resulting time can be used then in quantitative analytical methods for assessing the effectiveness of a technical protection system. The values of the fence system time resistance are very difficult to detect. Neither the technical standards give an exhaustive answer to questions related to time resistance of mechanical barriers. Some experiments were carried out with the aim to determine the time required for penetration of the selected wire fence obstacles. On the basis of the experiment results fuzzy rules were designed allowing, in conjunction with a suitable software tool, precise estimation of the wire fence barriers time resistance.

PREDIÇÃO DO RENDIMENTO DE CARCAÇA QUENTE EM BOVINOS DE CORTE POR SISTEMA ESPECIALISTA FUZZY

The aim of this study was develop a carcass yield estimator in beef cattle. The carcass yield was estimated applying an expert fuzzy model with two input variables (age and zebu genotype) and one output variable (carcass yield). The variables predicted by the model were compared with a database created from information drawn from scientific articles, from the years 2000 to 2006, whose contemplate the same variables of study. The minor deviations between the values experimentally measured yields of carcass values predicted by fuzzy modeling were observed in animals from groups with genotype 100% or 0% zebu, which showed deviations of 0.34 and 1.02 %, respectively. The influence of age on hot carcass yield was low compared to the deviations, while the influence of the degree of zebu blood was higher. The use of fuzzy modeling for the prediction of hot carcass yield in beef cattle is reliable with low standard deviation when compared to the literature.

Information technology as a catalyst of relationship marketing within a global context

Marketing has evolved since the beginning of the 20th century, thanks largely to information and communication technology (ICT). The transformation can be seen in changes to the economy and to technology and in the shifting preferences and needs of clients. With ICT, companies can deal directly with their clients, simulating real-life situations. This allows them to understand the necessities of the market in adopting their products and services thereby achieving a closer relationship and a competitive advantage by offering better value to their clients. At the same time, mathematics and fuzzy logic have facilitated the creation of the business models that are optimally adapted to the everyday situations that confront businesses in making their decisions in the present climate of uncertainty. This article presents the evolution of relationship marketing supported by ICT and the use of fuzzy models to simulate real situations in marketing. This work has two purposes: 1) to encourage scholars to continue researching on mathematical models applicable to relationship marketing; and 2) to invite marketing managers and ICT professionals, to develop systems to improve every day relationships with customers. Key words: Relationship marketing, evolution of information and communication technology, fuzzy models, FuzzyLog©.

Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis

In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution.

Land evaluation for major crops in the Indo-Gangetic plains and black soil regions using Fuzzy model.

Land evaluation is carried out to assess the suitability of land for a specific use. Land evaluation procedures focus increasingly on the use of quantitative procedures to enhance the qualitative interpretation of land resource surveys. Conventional Boolean retrieval of soil survey data and logical models for assessing land suitability, treat both spatial units and attribute value ranges as exactly specifiable quantities. They ignore the continuous nature of soil and landscape variation and uncertainties in measurement, which may result in the failure to correctly classify sites that just fail to match strictly defined requirements. The objective of this article is to apply fuzzy model to land suitability evaluation for major crops in the 15 benchmark sites of the Indo- Gangetic Plains (IGP) and 17 benchmark sites of the black soil regions (BSR). Minimum datasets of land characteristics considered relevant to rice and wheat in the IGP and cotton and soybean in the BSR were identified to enhance pragmatic value of land evaluation. The use of fuzzy model is intuitive, robust and helpful for land suitability evaluation and classification, especially in applications in which subtle differences in land characteristics are of a major interest, such as development of threshold values of land characteristics.

Fuzzy logic-based attenuation relationships of strong motion earthquake records

Fuzzy logic techniques have been widely used in civil and earthquake engineering applications in the past four decades. However, no thorough research studies were conducted to use them for deriving attenuation relationships for peak ground accelerations (PGA). This paper is an attempt to fill this gap by employing a fuzzy approach with fuzzy sets for earthquake magnitude and distance from source with the objective of proposing new ground motion attenuation models. Recent earthquake records from USA and Taiwan with magnitudes 5 or greater were used; and consisted of horizontal peak ground acceleration recorded on three different site conditions: rock, soil and soft soil. The use of Fuzzy models to quantify ground motion records, which are typically characterized by a high level of uncertainty, leads to a rational analytical tool capable of predicting accurate results. Testing of the fuzzy model with an independent data set confirmed its accuracy in predicting PGA values.

Construction of Fuzzy Map for Autonomous Mobile Robots Based on Fuzzy Confidence Model

This paper presents the use of fuzzy models to explicitly consider sensor uncertainty and finite resolution in solving the SLAM (simultaneous localization and mapping) problem for autonomous mobile robots. The approach establishes fuzzy confidence models in describing occupied obstacles and available space. The problem is transformed into an optimization task of minimizing the alignment error between newly scanned local fuzzy maps and selected parts of a developing global fuzzy map. In aligning local fuzzy maps into a global fuzzy map, we developed a prediction strategy to crop the most potential part from the sensed local fuzzy maps to be overlapped with the global fuzzy map. A mobile vehicle equipped with a laser range finder, the Hokuyo URG-04LX, is used to demonstrate the procedure of fuzzy map building. Experimental results show that the proposed architecture is effective in generating a comprehensive global fuzzy map, which is suitable for both human comprehension and path design during real-time navigation.

Розробка технології кольороподілу з використанням елементів нечіткої логіки у вигляді розмитих моделей

The management method by a raster by means of use of fuzzy models is offered in this article. For the maintenance the management were applied the pseudo-stochastic generator and a neural network. Changes of parametres are made in language of the user, with the help of semantic dictionary.

Quality control of hardware products based on the use of fuzzy models to describe technological inheritance

This article discusses formalization of the concept of technological inheritance as it pertains to quality control in hardware manufacturing. It is proposed that technological inheritance be described with the use of fuzzy models based on the theory of fuzzy sets.

Use of fuzzy models for predicting post-surgery complications in urological patients

Use of modern information technologies in combination with fuzzy decision logic makes it possible to generate prognostic models for high-confidence determination of the risk of post-surgery complications in urological patients. Problems of use of automated decision support systems based on fuzzy models for improving the accuracy of prediction of post-surgery complications in urological patients are discussed.