Assessment of risks of failure to achieve target values of indicators for an organization’s intellectual capital based on a fuzzy model

The aim of this research article is to develop and test a fuzzy model for the quantitative evaluation of university intellectual capital. The fuzzy model allows for the assessment of university intellectual capital as a whole, the main components of intellectual capital, the university’s abilities in various types of cognitive activities that contribute to the development of intellectual capital, and explicit and implicit factors of intellectual capital. The key distinguishing features of the model include: the formalization of explicit and implicit factors as linguistic variables and their translation into fuzzy sets; the use of fuzzy logic procedures in a hierarchical structure with possible cycles; the ability to obtain numerical evaluations of the dispersion of calculated values; and increased reliability of results by taking into account the levels of expertise of experts in specific areas of university activity using various smoothing functions. The results of testing the model on a large regional university are presented. Problematic areas in university activities regarding the development of intellectual capital are identified. The materials of the article are of interest to university leaders who receive a tool for a comprehensive assessment of intellectual capital and its components at all levels linked to the university’s development strategy.

The article is devoted to the importance of developing the company's intellectual capital as the main resource that ensures the company’s competitiveness in the digital economy. There are discussed the issues of intellectual capital management in a digital economy, systematizes the features of the implementation of management functions in relation to the intellectual capital of a metallurgical company, considers the features and problems of forming intellectual capital management in the context of management functions and types of intellectual capital. In the conditions of digitalization of economy the intellectual capital is proved to be the most important component of the effective functioning of the company. Intellectual capital is a necessary element of managing an organization and achieving success in a competitive market. The ever-increasing role of radical changes in information and communication technologies, economic, political and social spheres has been emphasized. The continuous process of personnel training allows companies to respond promptly to negative changes in the external environment and effectively manage human resources with intellectual capital. Intellectual capital in the narrow sense is a driver of career growth of a highly qualified employee, when a person acquires competencies leading to more effective communication, creativity, leadership, continuous learning and cognition. Knowledge, like any other resource, should be available at the right time, in the right place and to the right employees. Systematization of the fundamentals of managing the companies’ intellectual capital as a component of strategic management has been made. The concept of intellectual capital management system is clarified. The author’s vision of the prospects for the development of the company’s intellectual capital and the development of a comprehensive index for the development of intellectual capital are given. The indicator of the complex index of development of the intellectual capital of the company can be used in practice to assess the dynamics of the development of the intellectual capital of companies and its development trends in the future.

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.

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.

Evolving participatory learning (ePL) modeling joins the concepts of participatory learning and evolving fuzzy systems. It uses data streams to continuously adapt the structure and functionality of fuzzy models. This paper suggests an enhanced version of the ePL approach, called ePL+, which includes both an utility measure to shrink rule bases, and a variable cluster radius mechanism to improve the cluster structure. These features are useful in adaptive fuzzy rule-based modeling to recursively construct local fuzzy models with variable zone of influence. Moreover, ePL+ extends ePL to multi-input, multi-output fuzzy system modeling. Computational experiments considering financial returns volatility modeling and forecasting are conducted to compare the performance of the ePL+ approach with state of the art fuzzy modeling methods and with GARCH modeling. The experiments use actual data of S&P 500 and Ibovespa stock market indexes. The results suggest that the ePL+ approach is highly capable to model volatility dynamics, in a robust, flexible, parcimonious, and autonomous way.

This paper proposes a new unsupervised fuzzy clustering algorithm (NUFCA) to construct a novel online evolving Takagi–Sugeno (T-S) fuzzy model identification method and an adaptive predictive process control methodology. The proposed system identification approach consists of two main steps: antecedent T-S fuzzy model parameters identification and consequent parameters identification. The NUFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modelling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system; then the recursive least squares method is exploited to obtain initialization type consequent parameters and to construct a method for on-line fuzzy model identification. The integration of the proposed adaptive identification method with the generalized predictive control results in an effective adaptive predictive fuzzy control methodology. For better demonstration of the robustness and efficiency of the proposed methodology, it is applied to the identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment plant (WWTP); and to control a simulated continuous stirred tank reactor (CSTR), and a real experimental setup composed of two coupled DC motors. The results show that the developed evolving T-S fuzzy model methodology can identify nonlinear systems satisfactorily and can be successfully used for a prediction model of the process for the generalized predictive controller. It is also shown that the algorithm is robust to changes in the initial parameters, and to unexpected disturbances.

A complex process which is difficult to be mathematically expressed can be described by a set of fuzzy inference rules, and fuzzy modeling has been regarded as one of the key problems in fuzzy systems research. A quick and accurate fuzzy modeling method is presented in accordance with the characteristics of SISO systems. That is, the domain of discourse of the input variable is divided firstly according to the changing degree of the process output while the input variable changes, and based on the above, dividing the total number and the premise parameters of the fuzzy rules can be determined, then because the presented fuzzy model can be expressed as a fuzzy neural network which is a feedforward neural network, so the BP algorithm is applied to obtain the consequent parameters of the fuzzy rules. The effectiveness of the presented fuzzy modeling method and the generalization ability of the fuzzy rules model are demonstrated by a simulation example.

In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed points provided by recognition. The authors assess the performance of this method for both nonsparse (compact blob-like) and sparse (thin tubular) objects in the thorax. The results of eight thoracic organs on 30 real images are presented. Overall, the delineation accuracy in terms of mean false positive and false negative volume fractions is 0.34% and 4.02%, respectively, for nonsparse objects, and 0.16% and 12.6%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 1.31 and 2.28 mm, respectively. The hierarchical structure and location relation integrated into the model provide the initial pose for registration and make the recognition process efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both nonsparse and sparse organs. Tailoring the registration process for each organ by specialized similarity criteria and updating the organ intensity properties based on refined recognition improve the overall segmentation process.

The fuzzy comprehensive model was established with fuzzy c-mean cluster method. Based on this fuzzy model, the operation state of anaerobic system, divided into stable, relatively stable, relatively unstable and unstable, was estimated. The results showed that the fuzzy comprehensive evaluation model is suitable to judge different operation state in anaerobic system, and to discover the relationship among factors of system parameters and evaluation results. The fuzzy model is objective and gives all-round and more accurate reflection of operation state. It is help for improving the management and control effect of anaerobic system.

PurposeThe purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties.Design/methodology/approachThe dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model.FindingsAccording to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties.Originality/valueThis paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

In this paper we proposed a fuzzy neural network model which can embody a fuzzy Takagi-Sugeno model and carry out fuzzy inference and support structure of fuzzy rules. The algorithm of model properties improvement consists of new origin procedures namely input space partition, fuzzy terms number and rule number extending, low-effective fuzzy terms and rules extraction and consequent structure identification. In the proposed fuzzy modeling method we first design a rough initial fuzzy model with complete partition of input variable space (or initial partition based on expert knowledge). Then a fuzzy neural network is constructed based on rough fuzzy model. By learning of the neural network we can tune of embedded initial fuzzy model. Next, the additional identifying procedure is introduced based on additional partition of fuzzy input space to improve the properties of initial fuzzy model and to decrease the model error. In final part of identification some low-effective terms and rules are extracted and final rule based model is formed. To apply the new identifying procedures and to introduce possibilities of variability of their properties some parameters have to be put in. The strategy of such parameter optimization is provided by new advanced genetic algorithm. Criterion and cost function has been selected as global fuzzy-neuro model error. To show the applicability of new method and to make a possibility to real systems modeling, we designed the fuzzy-neural network programme tool FUZNET. There were two case studies performed: the first case study presents the prediction of Mackey-Glass time series with using fuzzy-neural regression model (FNRM) predictor; the second case study presents task of a coke-oven gas cooler modeling.

One of the best ways for better understanding of biological experiments is mathematical modeling. Modeling cancer is one of the complicated biological modeling that has uncertainty. Therefore, fuzzy models have studied because of their application in achievement uncertainty in modeling. Overall, the main purpose of this modeling is creating a new view of complex phenomena. In this paper, fuzzy differential equation model consisting of tumor, the immune system and normal cells has been studied. Model derived from a classical model DePillis in 2003, which some parameters from a clinical point of view can be described in the region. In this model, by considering fuzzy parameters from clinical point of view, the three-dimensional fuzzy tumor cells in terms of time and membership function are pictured and region of uncertainties are determined. To access the uncertainty area we use fuzzy differential inclusion method that is one of the including methods of solving differential equations. Also, different initial conditions on the model are inserted and the results of them are analyzed because tumor has different treatment in different initial conditions. Results show that fuzzy models in the best way justify what happens in the reality.

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as "social sensor" and "social IoT," these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber–physical system (CPS)-oriented information computing sublimate to the cyber–physical–social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber–physical–social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss the challenges for future research directions.

Fuzzy risk modeling of process operations in the oil and gas refineries

Abstract. In this paper, possibilities of using fuzzy system models as a part of model-based fault diagnosis systems are discussed. Methods and experiences of using fuzzy system models at different stages of fault diagnosis systems are presented. In this study, fuzzy system models are used in fault detection and inverse fuzzy system models in fault localization. Inverse fuzzy models are discussed in the light of real-world applications. The mathematical theory is skipped entirely. Additionally we present some examples of using a fuzzy system model in an interface between a fault diagnosis system and a (human) operator. We also briefly discuss a total management of the life-cycle of mechatronic systems. We divide the life-cycle of a mechatronic system into four stages: design, development, installation (integration), and operation and maintenance. When a mechatronic device is being designed and built, it is difficult to analyze the behavior of the device before testing it in practice. It is common that many prototypes of the device are tested at different stages of the development process. The main idea of our approach is that a fuzzy model of a system is built as early as possible, and the model is used, for example, in analyzing the validity of design work done so far. The model is updated and used for different purposes throughout the development and installation process. Finally, the model is used as a part of fault diagnosis and control systems.Fault DiagnosisKnowledge Based SystemsFuzzy Modeling