Fuzzy Modeling Method Research Articles

Нечеткие реляционные когнитивные темпоральные модели для анализа и прогнозирования состояния сложных технических систем

The effectiveness of fuzzy cognitive modeling methods for analyzing and predicting the state of complex technical systems (STS) is justified by the following reasons: significant interdependence, non-linear nature and incompleteness of information about the mutual influence of the analyzed parameters of the CTS; a variety of effects of internal and external factors on the CTS; complexity and cost of conducting experimental studies during the operation of these systems. The main limitations of fuzzy cognitive models for modeling STS dynamics are: the complexity of taking into account the mutual influence of parameters with their different time lags relative to each other; the need for their constant operational adjustment and training of component models for all parameters during the operation of the CTS. In this paper, Fuzzy Relational Cognitive Temporal Models (FRCTM) are developed. These models combine the advantages of various types of fuzzy cognitive models, and at the same time neutralize the main limitations of the analysis and prediction of the state of the CTS, which are inherent in the well- known fuzzy cognitive models. The paper also proposes models of system dynamics that take into account the specifics of the FRCTM. We have also developed an approach and implemented a method for calculating fuzzy dependencies in vector-matrix form for dynamic modeling of the CTS. The proposed method makes it possible to solve the problems of increasing the uncertainty of the results and the output of fuzzy values of the FRCTM concepts beyond the ranges of the base sets due to the execution of mass iterative computations. An example of modeling heterogeneous electromechanical systems based on FRCTM is given. The results obtained are the basis for solving a whole range of tasks of analysis, predictive evaluation, modeling of different scenarios of the functioning and development of heterogeneous electromechanical systems for various system factors, operating modes and external conditions.

COMPLEX MODELING OF TECHNICAL CONDITION AND REGIMES OF VOLTAGE REGULATION OF CONTROL DEVICES UNDER LOAD OF POWER TRANSFORMERS

The scientific work considers issues (tasks) of the technical condition assessment and voltage regulation in power system load nodes using power transformers on-load tap-changer devices by fuzzy modeling methods. A linguistic model of TC class determining and decision-making on the operation strategy for OLTC devices is presented. A fuzzy algorithm for voltage reg-ulation in load nodes by OLTC devices is proposed, taking into account resource constraints. A voltage control modes test study under changing the nodal load was conducted for the consumers power supply system of the distribution network

Review on agricultural non-point source pollution monitoring sections layout and pollutant loading estimation in small watershed

流域尺度面源污染的监测是系统认识农业面源污染的发生、迁移及转化过程, 并对其进行有效控制的重要基础. 当前, 田块尺度的面源污染监测方法比较成熟, 而流域尺度的监测, 尤其是监测断面的布设及采样频率设置等方面的研究较少. 本文详细梳理了小流域采样断面布设、采样频率优化和河流断面通量估算3方面的主要进展. 1)从小流域样点布设来看, 包括常规监测采样设计、针对性监测采样设计和融合前两种类型的监测采样设计3种类型, 点位布设方法上有遗传算法、模糊逻辑法、熵值法和模型法等, 样品采集方式包括随机采样、复合采样、综合采样以及连续采样4种类型, 其中复合采样应用广泛；2)从采样频率来看, 1~2周一次的采样频率即可精准获取污染负荷通量, 若需要进一步提高精度, 可在水文/水质变异大的时期提高监测频率及在特殊断面加密布点；3)在通量估算上, 当前主要的计算方法有平均法、插值法和回归/曲线法3类方法, 其中流量加权的浓度估计法、插值算法和LOADEST法是简便且精确的方法, 方法的选择上也可根据不同时期流域污染源特征进一步优化.;Watershed-scale non-point source pollution monitoring is an important basis for systematically understanding the occurrence, transportation and transformation processes of agricultural non-point source pollution and its effective control. Presently methods monitoring non-point source pollution at field scale are relatively mature, while methods applied at watershed scale, particularly the layout of monitoring section and sampling frequency setting, are rarely studied. This paper reviewed the main progress of sampling section layout, sampling frequency optimization and loading estimation of river sections. From the perspective of sampling layout in small watershed, there are three types of monitoring design including probabilistic design, targeted design and both. Sampling points were located with genetic algorithm, fuzzy logic method, entropy and modelling method, etc. Sample collection methods include random sampling, compound sampling, comprehensive sampling and continuous sampling. Compound sampling is widely used. Concerning the sampling frequency, once every 1-2 weeks is sufficient to obtain accurate pollution loading. If higher accuracy is necessary, the monitoring frequency and special sections can be increased when hydrology/water quality variation is large. For loading estimation, average method, interpolation method and regression or curve method were widely used. Concentration estimation algorithm by flow-weighted, interpolation and LOADEST are simple and exact. Selection of methods could be further optimized based on the characteristics of pollution sources in different periods of the basin.

Fuzzy Logic-Based Modeling Method for Regional Multi-Ship Collision Risk Assessment Considering Impact of Ship Crossing Angle and Navigational Environment

Fuzzy Logic-Based Modeling Method for Regional Multi-Ship Collision Risk Assessment Considering Impact of Ship Crossing Angle and Navigational Environment

Comparison of data-based models for prediction and optimization of energy consumption in electric arc furnace (EAF)

This paper addresses the problem of data-based optimization of electric arc furnace (EAF) energy consumption. In the steel industry, optimization of production processes could lead to savings in energy and material consumption. Using data from EAF batches produced at the SIJ Acroni steel plant, the consumption of electrical energy during melting was analysed. For each batch, different parameters and signals were measured, such as the weight of the scrap, injected oxygen, added carbon, energy consumption, etc. After the preprocessing phase (detection of anomalies and outliers), the most influential regressors were analysed and selected for further modelling and prediction. In the modelling phase, we focused on evolving fuzzy modelling method in comparison with some established machine learning methods. The obtained static models were used to predict the total energy consumption of the current batch. All models were trained with 70% of data and validated and compared with 30% of data. The experimental results show that the proposed models can efficiently predict the energy consumption, which can be used to reduce the energy consumption and increase the overall efficiency of the electric steel mill.

Fuzzy modelling of the transportation logistics processes

The relevance of mechanisms and fuzzy modeling methods used in transport and logistics processes is justified. The logic of the transport and logistics chains study by their decomposition into separate economic entities with subsequent synthesis, with taking into account their conflicting interests, is presented. The task of managing transport and logistics processes is set within the framework of the conceptual positions of the fuzzy sets theory: the use of linguistic variables, the concept of a fuzzy set, fuzzy inference, implemented on the basis of fuzzy sets operations. In this study, the fuzzy modeling on the bottom-up conclusions from the premises to the conclusion was used. Various methods of implication and defuzzification have been analyzed. An iterative procedure for managing transport and logistics chains and their links has been proposed, which ensures the adaptation of the process to the specified performance indicators. A calculated example is given for a marshalling yard as a link in the transport and logistics chain.

Adaptive event-based robust passive fault tolerant control for nonlinear lateral stability of autonomous electric vehicles with asynchronous constraints

This work solves the robust passive fault-tolerant control problem for autonomous electric vehicles based on an adaptive event triggered mechanism. Firstly, given the system uncertainties from the tire dynamics and the longitudinal speed, the T–S fuzzy model method is used to approximate the vehicle lateral dynamics. Secondly, taking the communication constraints caused by band-limited networks into account, an adaptive event-triggered scheme is introduced in the process of the control design. Moreover, the asynchronous constraint of the weight function between the controller and system is considered. Thirdly, considering that the actuator faults are inevitably encountered in the control system, a robust passive fault-tolerant control method is proposed to improve vehicle performances. Finally, simulations are carried out to illustrate the effectiveness and robustness of the proposed approach.

Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network

The coordinated control systems (CCS) in ultra-supercritical thermal power unit, like many other industrial systems, is a complex multivariable system with severe nonlinearity, strong multivariable coupling and uncertainties. In order to meet the requirements of operational stability, economy. etc in ultra-supercritical unit, it is necessary to establish its accurate mathematical model and further design the advanced controller. Against this background, a new fuzzy neural network modeling method is proposed in this paper. First of all, the incremental model is considered separately to improve the rationality of the local linear model structure. Then, the parameters in antecedent part is initialized by a kernel k-means++ algorithm, in which Xie-Beni index is used to optimize the number of fuzzy rules. Finally, supervised adaptive gradient descent algorithm and artificial immune particle swarm optimization algorithm work in stages to complete the training of the consequent part parameters. The proposed modeling method in this paper is applied to a 1000 MW unit in China and shows satisfactory accuracy. In the established model, the MSE of power output, main steam pressure and separator outlet steam temperature are 0.0099, 1.21E-4, 0.0023, respectively. Both numerical and graphical simulation results confirm the effectiveness of the presented fuzzy neural network in modeling.

Transductive Multiview Modeling With Interpretable Rules, Matrix Factorization, and Cooperative Learning.

Multiview fuzzy systems aim to deal with fuzzy modeling in multiview scenarios effectively and to obtain the interpretable model through multiview learning. However, current studies of multiview fuzzy systems still face several challenges, one of which is how to achieve efficient collaboration between multiple views when there are few labeled data. To address this challenge, this article explores a novel transductive multiview fuzzy modeling method. The dependency on labeled data is reduced by integrating transductive learning into the fuzzy model to simultaneously learn both the model and the labels using a novel learning criterion. Matrix factorization is incorporated to further improve the performance of the fuzzy model. In addition, collaborative learning between multiple views is used to enhance the robustness of the model. The experimental results indicate that the proposed method is highly competitive with other multiview learning methods.

A New Model in Designing the Professional Competence System of the Petrochemical Industry with a Sustainable Development Approach

The purpose of this study is to identify and prioritize professional competency indicators and strategies to improve it in the petrochemical industry. This research is a combination because it uses qualitative and quantitative methods in order to collect and analyze data. To identify indices and indigenous components of professional competence, a qualitative and meta-analysis approach and in a quantitative approach, the fuzzy structural equation modeling method has been used in the petrochemical industry. Based on data, a new model with a sustainable development approach has been reached. Which includes the following components: Creativity and Innovation, General Professional Performance, Analytical Skills, Specific Professional Skills, General Professional Skills, Management Skills, Job Information and Awareness, Psychological Competencies, Perceived Self-Efficacy, Adaptability, Basic Expertise, Job Commitment, Professional attitude, tendency to career development, Intrinsic individual characteristics, Increasing factors, Intrinsic individual characteristics, Responsibility, Professional motivations. The results showed that the dimensions of psychological capabilities and general professional performance were located at the basic and lower levels and were identified as the main and most effective dimensions in the development of the most professional competence of the petrochemical industry. And the two elements of increasing competence and adaptability are the least important among the studied dimensions. The components of job information and awareness, career development desire, and managerial skills were identified as the most active components because these components have the highest interactions with other components.

H∞ estimation for non-linear positive switched systems with time-varying delay

Abstract For non-linear positive switched systems (NPSSs) with time-varying delay, the $H_{\infty }$ estimation problem is addressed in this paper. Firstly, in light of T-S fuzzy modeling method, the considered NPSS is equivalently transformed into a switched positive T-S fuzzy system. Then, a less conservative $H_{\infty }$ performance co1ndition is provided for the error system via presenting a new piecewise quadratic copositive Lyapunov–Krasovskii functional. Furthermore, an observer is constructed to make the underlying system exponentially stable with a preset $H_{\infty }$ performance and an iterative optimization algorithm is proposed to obtain the observer parameters. To verify the effectiveness of the theoretical results, two examples are finally presented.

$${L_\infty }$$ Fault Estimation and Fault-Tolerant Control for Nonlinear Systems by T–S Fuzzy Model Method with Local Nonlinear Models

This paper investigates the problems of the observer-based fault estimation (FE) and fault-tolerant control (FTC) for nonlinear systems subjected to external disturbances by the T–S fuzzy model method with local nonlinear models. Firstly, an FE strategy is proposed based on the unknown input observer technology, where the local nonlinear terms can be decoupled from the FE error system for relaxing the design of observer. Then, by using the FE information, a fuzzy fault-tolerant controller is designed to guarantee the stability of the system. Additionally, in the design schemes of the FE observer and the fault-tolerant controller, the $${L_\infty }$$ method is used to deal with the problems of FE and FTC for the system with persistent disturbance such that the robustness of the system against the persistent external disturbance can be increased. Compared with the traditional $${H_\infty }$$ method which is only applicable for dealing with the energy bounded signals, the $${L_\infty }$$ FTC strategy proposed in this paper can deal with the persistent disturbance such that the shortcomings of the traditional $${H_\infty }$$ approach are overcome. Finally, the effectiveness of the proposed method is verified by simulation results.

Output space transfer based multi-input multi-output Takagi–Sugeno fuzzy modeling for estimation of molten iron quality in blast furnace

The molten iron quality (MIQ) in the blast furnace (BF) needs on-site sampling and laboratory tests, which is very unfavorable to the control. To estimate multiple molten iron quality indicators (MIQIs) such as molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]) in real-time, some data-driven multi-input multi-output (MIMO) modeling methods have lately been developed. However, those modeling methods ignore the inter-indicator correlation. Moreover, the above modeling methods do not consider the processing strategy for the samples with missing indicators. To conquer the above two problems in the process of modeling, this paper proposed an output space transfer based MIMO Takagi–Sugeno (T–S) fuzzy modeling method for the four MIQIs. Through sparse learning of the correlation matrix, the inter-indicator correlation is specifically modeled. Due to the full use of inter-indicator correlation, the impact of missing indicators on modeling accuracy is greatly reduced. An effective alternative optimization algorithm is also provided for the corresponding intractable optimization problem. The actual data test shows that the proposed method can greatly improve the accuracy of multiple MIQIs estimations.

Fuzzy cognitive modeling of heterogeneous electromechanical systems

The article presents a method of fuzzy cognitive modeling for heterogeneous electromechanical systems (HEMSs) in the management of innovative design solutions. During the operation of the HEMSs, as a result of their operational aging, the properties of the windings parametric matrices and the HEMSs vector space properties change. Periodic testing of the HEMSs vector space allows obtaining reliable information about the current technical condition of the HEMSs, about its changes during operation and about the risks of operating capability loss. At the same time (I) the presence of proportional changes in signals during sequential testing indicates the homogeneous operational aging of the HEMSs and its rate; (II) a disproportionate change in one of the signals indicates the damage or the development of a heterogeneous aging process; (III) a change in signals with a change in the angular position of the rotor indicates worn bearings or damage of the HEMSs rotor. The article presents the HEMSs model, describes the method for the topological research of the vector space and the method for forming the diagnostic matrices. The deviations of their elements are fuzzy due to the uncertainty of the load, influencing environmental factors and unstable supply voltages. Therefore, for predictive estimation of the HEMSs state, it is proposed to use fuzzy relational cognitive models that allow implementing a completely fuzzy approach to modeling problem situations in these systems. The presented data confirm the growth of the HEMSs heterogeneity under conditions of uncertainty of external influences. The proposed method for predictive estimation of the HEMSs state, based on fuzzy relational cognitive models, provides resistance to an increase in the uncertainty of the estimation results for various models of system dynamics due to a reasonable set of fuzzy vector-matrix operations.

A review on fuzzy preference modeling methods for group decision-making

With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is still widely used by researchers until now. Subsequently, based on fuzzy theory, the researchers also proposed linguistic term sets and cloud model. These methods have different representation domains, and are applicable to different decision-making environment. In this paper we give a review of classical fuzzy preference modeling methods and its latest extensions and variants. After the presentation of comparative analyses on the existing methods, we figure out some current challenges and possible future development directions in the field of fuzzy preference modeling.

Fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training

Aiming at the false correlation problems under set number limit condition in the fuzzy control mathematic modelling process, combined with dynamic particle swarm optimisation training algorithm, the traditional fuzzy control mathematical model based on the Nash equilibrium solution method is difficult to converge to the optimal solution of the state space, leading to bad control performance. This paper proposes the fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training, constructs the general structure model of fuzzy control, describes the standard particle algorithm under the constraint of learning samples of random functional, obtains the global optimal solution of control domain of fuzzy control parameters, and conducts particle swarm optimisation training by adopting the position vector fitness updating method. The research results show that the new method can make every step state update get more effective observation information, reduce the error caused by the difficult use of observation data, reduce the computation cost and improve the accuracy of fuzzy control.

РОЗРОБЛЕННЯ МОДЕЛЕЙ УПРАВЛІННЯ РИЗИКАМИ У ПРОЄКТАХ КІБЕРБЕЗПЕКИ З ВИКОРИСТАННЯМ НЕЧІТКОЇ ЛОГІКИ

This article is devoted to the analysis of the conditions for the implementation of startup projects in the field of cybersecurity, which are currently implemented and funded by the state through the use of modern information technology. There are many different startup projects in this field, related to the rapid development of information technology and information security technology. However, the opportunities for public funding and attracted private funding for such projects are limited, which in some way hinders opportunities for further development. Thus, there is a task of selecting the best startup projects in the field of cybersecurity, which in turn requires the development of the necessary models and modeling methods. This paper investigates and analyzes information sources that show that the issue of evaluating the effectiveness of IT startups is not sufficiently addressed, especially for the use of products of such projects in cybersecurity. This imposes additional requirements and restrictions on the IT products of such projects and on the management processes of such projects. In addition, the future of cybersecurity startups is associated with many parameters that are highly conditional and predictable in the early stages of project review. Therefore, to accept the project for consideration, it is advisable to use fuzzy modeling methods. By using the fuzzy set method, it is possible to use fuzzy variables that reflect the uncertainty of some parameters of such projects. The proposed research methodology is based on the analysis of project efficiency and the use of fuzzy set methods. For this purpose, membership functions are constructed, which establish the degree of belonging of a fuzzy set. The trapezoid model is chosen as the function type and the parameters corresponding to the pessimistic, basic and optimistic scenarios are set. The novelty of the work is to determine the degree of risk of a startup project, which depends on the criterion of project effectiveness. The paper proves the dependence of the cybersecurity project risk indicator on the value of the project effectiveness criterion. The proposed approach has shown its feasibility and can be used to analyze startup projects by scientists, project managers, entrepreneurs and investors, cybersecurity professionals

Robust Stochastic Observer-Based Attack-Tolerant Missile Guidance Control Design Under Malicious Actuator and Sensor Attacks

In this study, the robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> stochastic observer-based attack-tolerant guidance control strategy is designed for the nonlinear stochastic missile guidance control system under the external disturbance and measurement noise as well as actuator attack signal and sensor attack signal. To simplify the attack signal estimation, a novel nonsingular smoothed dynamic model is introduced to efficiently describe the actuator and sensor attack signals. Consequently, the state/attack signal estimation can be easily achieved by using conventional Luenberger observer. Next, to attenuate the effect of external disturbance, measurement noise and approximation errors of attack signal on the missile guidance control system, the robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> attack-tolerant guidance control performance is considered and the design condition is derived in terms of nonlinear Hamilton-Jacobi inequality (HJI) constrained problem. Since HJI cannot be easily solved analytically or numerically, the Takagi-Sugeno (T-S) fuzzy modelling method is utilized to facilitate the robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> attack-tolerant guidance control strategy design. Thereafter, the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> observer-based attack-tolerant control design problem is transformed into linear matrix inequalities problem (LMIP) which can be solved very efficiently by using the convex optimization techniques. Simulation example, with the comparison between the proposed method and conventional robust missile guidance strategy, is given to illustrate the design procedures and validate the performance of the proposed method.

Fuzzy cognitive modeling of product import and export impacts on food security

The modern economy of the Russian Federation is in a difficult foreign economic situation, due to the sanctions pressure forced import substitution in a number of sectors of agricultural production. The sanctions imposed by developed countries led to a response from Russia, which imposed an embargo on certain groups of agricultural raw materials and food products. The aim of the research is to develop and implement software for fuzzy cognitive modeling of the level of food security in the context of import substitution, taking into account food exports. To determine the perspective directions of numerical estimation and increase the level of FS in modern Russia, by modeling and justifying import substitution, the method of fuzzy cognitive mathematical modeling is used, which allows us to build models of the evolution of the emerging level of interaction of key factors of the simulated system. The conducted cognitive modeling of the FS level showed that import substitution increases infrastructure costs and has a positive effect on the overall economic situation, as it determines the development of production. The study of the mutual influence of the import substitution factor, within the framework of ensuring the required level of FS, is limited by the existing conditions, largely determined by the sanctions pressure on the Russian Federation. It is shown that a promising area of research is the analysis of the sensitivity of the cognitive model to the influence of all key factors, as well as the search for factors that are most responsive to the control actions of the governing bodies.

Fuzzy control mathematical modelling method based on dynamic particle swarm optimisation training

Aiming at the false correlation problems under set number limit condition in the fuzzy control mathematic modelling process, combined with dynamic particle swarm optimisation training algorithm, th...