Structure Of Fuzzy Model Research Articles

Risk assessment of COVID-19 based on a new structure of neural fuzzy probabilistic model

The risk assessment of the COVID-19 infection can save so many lives, reduce treatment costs, and increase public health. The unknown nature of the COVID-19 infection, the high impreciseness of available information, and not simply recognizing the relevant factors and their effectiveness may cause overestimating and underestimating of factors. This paper puts forward a development of a model with fewer limitations that are more consistent with progressive knowledge about COVID-19. Dealing with the situation of updating the statistical dataset daily, the proposed approach can effectively use the subjectivity inherent in the fuzzy probability interpretation of risk factors using expert knowledge in addition to the statistical dataset. Second, to this uncertainty handling improvement, a specificity-based parameter learning based on the learning network is also added to deal with the complexity aspect of the COVID-19 infection. The learning process helps the proposed structure better adjust the effectiveness of factors. From the achieved results, it is verified that people with advanced age, those with chronic obstructive pulmonary disease, lung cancer, and those having cancer treatments are at higher risk of death if they are infected by COVID-19. Undoubtedly, for vaccination, these three groups should be considered in order to prevent death situations.

Development of a method for assessment and forecasting of the radio electronic environment

Decision making support systems (DSS) are actively used in all spheres of human life. The system of the electronic environment analysis is not an exception. However, there are a number of problems in the analysis of the electronic environment, for example: the signals are analyzed in a complex electronic environment against the background of intentional and natural interference. Input signals do not match the standards, and their interpretation depends on the experience of the operator (expert), the completeness of additional information on a particular task (uncertainty condition). The best solution in this situation is found in the integration with the data of the information system analysis of the electronic environment, artificial neural networks and fuzzy cognitive models. Their advantages are also the ability to work in real time and quick adaptation to specific situations. The article develops a method for assessing and forecasting the electronic environment.
 Improving the efficiency of evaluation information processing is achieved through the use of evolving neuro-fuzzy artificial neural networks; learning not only the synaptic weights of the artificial neural network, the type and parameters of the membership function. The efficiency of information processing is also achieved through training in the architecture of artificial neural networks; taking into account the type of uncertainty of the information that has to be assessed; synthesis of rational structure of fuzzy cognitive model. It reduces the computational complexity of decision-making; has no accumulation of learning error of artificial neural networks as a result of processing the information coming to the input of artificial neural networks. The example of assessing the state of the electronic environment showed an increase in the efficiency of assessment at the level of 15–25 % on the efficiency of information processing

Analysis on the hidden cost of prefabricated buildings based on FISM-BN.

Facing the pressure of environment, sustainable development is the demand of the current construction industry development. Prefabricated construction technologies has been actively promoted in China. Cost has always been one of the important factors in the development of prefabricated buildings. The hidden cost of prefabricated buildings has a great impact on the total cost of the project, and it exists in the whole process of building construction. In this paper innovatively studies the cost of prefabricated buildings from the perspective of hidden cost. In order to analysis the hidden cost of prefabricated buildings, the influencing factor index system in terms of design, management, technology, policy and environment has been established, which includes 13 factors in total. And the hidden cost analysis model has been proposed based on FISM-BN, this model combines fuzzy interpretive structure model(FISM) with Bayesian network(BN). This model can comprehensively analyze the hidden cost through the combination of qualitative and quantitative methods. And the analysis process is dynamic, not fixed at a certain point in time to analyze the cost. We can get the internal logical relationship among the influencing factors of the hidden cost, and present it in the form of intuitive chart by FISM-BN. Furthermore the model could not only predict the probability of the hidden cost of prefabricated buildings and realize in-time control through causal reasoning, but also predict the posterior probability of other influencing factors through diagnostic reasoning when the hidden cost occurs and find out the key factors that lead to the hidden cost. Then the final influencing factors are determined after one by one check. Finally, the model is demonstrated on the hidden cost analysis of prefabricated buildings the probability of recessive cost is 26%. In the analysis and control of the hidden cost of prefabricated buildings, scientific and effective decision-making and reference opinions are provided for managers.

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.

Fuzzy modelling of combustion efficiency and control of excess air flow case study: 320-MW steam unit/Isfahan Power Plant/Iran

Abstract One of the most critical factors affecting boiler efficiency and hazardous-gas-emission reduction is the volume of excess air mixed with fuel. A knowledge-based approach is proposed to model the efficiency of a 320-MW natural-gas-fired steam power plant in Isfahan, Iran by applying fuzzy-modelling techniques to control the boiler efficiency. This model is based on fuel and air entering the boiler. First, the fuzzy-model structure is identified by applying the fuzzy rules obtained from an experienced human operator. The proposed method is then optimized using a genetic algorithm to increase the fuzzy-model accuracy. The results indicate that, by applying a genetic algorithm, the precision of the proposed fuzzy model increases. The error between the actual efficiency of the plant and the output efficiency of the proposed model is low. This model is developed by applying the fuzzy rules and modelling-related calculations. Finally, to optimize the efficiency of the boiler, a fuzzy proportional-integral controller is designed. The closed-loop control simulations are run by applying both the proposed controller and the manual controller to demonstrate the influence of the suggested method. The simulation outcomes indicate that the recommended controller adjusts the excess-air percentage correctly and increases the unit efficiency by 0.70%, significantly reducing fuel consumption.

Application of green supply chain management in the oil Industries: Modeling and performance analysis

Environmental concerns relating to production affairs have made various organizations use green practices in different processes of supply chain, because the green supply chain management (GSCM) is considered as an important organizational philosophy to decrease environmental risks and as a preventive approach in order to increase environmental performance and achievement of competitive advantages for organizations. The purpose of the present article is to design an interactive model for the practices of GSCM and its application to clustering oil industries for analyzing their green performance. Therefore, the literature was studied and a total of fifteen practices were obtained using experts’ opinions in academic and oil industry professionals. In next, the fuzzy interpretative structural modeling (FISM) approach was utilized so as to determine the relationship between the practices through considering the linguistic ambiguities of judgments and designing the structural model. The existing relationships within the structural model were studied and tested by means of structural equation modeling (SEM). After that, the relative importance of each practice was calculated by applying fuzzy analysis network process (FANP). In the next, the oil industries were categorized in two clusters using the K-means algorithm aggregated to the particle swarm optimization algorithm. Results of the present study showed that “legal requirements and regulations”, “intra-organizational environmental management”, “green design” and “green technology” are of root and influential practices with relatively more importance than others; in addition, it was cleared that the first cluster industries have high performance whereas the second ones have medium performance from the viewpoint of considering the practices of GSCM. Finally, the discriminant function designed to forecasting environment performance of the oil industries and membership to clusters for each of them.

Management Path of Concrete Beam Bridge in China from the Perspective of Sustainable Development

More and more bridges have entered the maintenance stage, which has potential collapse hazards and threatens life and property safety. More attention has been paid to the improvement of maintenance management levels during the operation period in terms of extending the service life of the bridge, but less attention has been paid to it from the perspective of the whole life cycle. One hundred and ninety examples of concrete beam bridges in China were collected, based on which the collapse characteristics and collapse causes of concrete beam bridges were analyzed. The causes of bridge collapse come from all stages of bridge life cycle, including environmental factors and human factors. Moreover, the effects of the previous phase carry over to the next. Superficially, poor maintenance management during an operation led to bridge collapse. However, the root cause may have occurred at an earlier stage. On this basis, a fuzzy interpretation structure model (FISM) for concrete beam bridge deterioration is conducted. The model can decompose the complex and messy relationship among the factors of bridge collapse into a clear, multi–level and hierarchical structure. Compared with qualitative analysis, an ISM chart can directly reflect the relationship between collapse factors, which is convenient for further analysis. Poor maintenance management during operation is the direct cause, while improper planning, imperfect standards and weak supervision in the early stage are the fundamental causes. Finally, in order to improve the sustainability of concrete beam bridges scientifically, management suggestions are put forward for the participants involved in each stage of the bridge’s life cycle.

Genetic-Programming-Based Architecture of Fuzzy Modeling: Towards Coping With High-Dimensional Data

This article is concerned with the development of fuzzy models realized with the aid of genetic programming (GP). The proposed architecture employs GP to form fuzzy logic expressions involving logic operators and information granules (fuzzy sets) located in the input space, used to predict information granules located in the output space. We propose an architecture realizing logic processing, with the structural optimization of the model accomplished by a multitree genetic programming and the parametric optimization completed by gradient-based learning. The granulation of information used in this architecture is developed using the Fuzzy C-means clustering algorithm. The novelty of this study is two-fold: 1) it comes with the flexibility of the logic-oriented structure of fuzzy models, and 2) our architecture is designed to handle high-dimensional data by alleviating the detrimental effect of distance concentration hampering the effectiveness of standard Takagi-Sugeno-Kang fuzzy rule-based models. The article is illustrated through some experiments that provide a detailed insight into the performance of the fuzzy models. A comprehensive comparative analysis is also covered.

Critical assessment of construction contract administration using fuzzy structural equation modeling

PurposeThis paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.Design/methodology/approachThe research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.FindingsThe data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.Originality/valueThe contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.

Adaptive neuro fuzzy inference system to predict average asphaltene particle diameter case study: in thermal de-asphalting process

In thermal de-asphalting procedure, where asphaltene particles are aggregated through heating crude oil at an elevated temperature, predicting the asphaltene particles’ diameter is of major importance. In this study, thermal de-asphalting process is modeled using adaptive neuro fuzzy inference system by considering isolating temperature, crude oil API and asphaltene content as inputs to predict asphaltene particles’ average diameter. The experimental data measured in a custom built thermal de-asphalting set-up, are applied to construct neuro fuzzy model structure. The average relative error of proposed model, for all data points is 3.06%, indicating an excellent agreement between model predictions and experimental values.

Towards understanding key enablers to green humanitarian supply chain management practices

PurposeHumanitarian supply chains (HSCs) by their very nature require urgent reaction to unforeseeable needs, making it difficult to properly plan for the support of actual demands. As such, integrating sustainability into traditional HSC practices continues to present a challenge to governments, nongovernmental organizations (NGOs) and other humanitarian-related agencies. This study focuses on identifying and categorizing the leading enablers to green humanitarian supply chains (GHSCs) and proposes a model for improving the responsiveness based upon a fuzzy total interpretive structural modelling approach.Design/methodology/approachTotal interpretive structural modelling (TISM) uses group decision-making to identify contextual relationships among each pair of enablers and elucidates the nature of each underlying relationship. The fuzzy TISM shows the level of strength (very high influence, high influence, low influence and very low influence) of each enabler in relation to other enablers, which can help to inform management decision-making.FindingsGHSC management requires strategic planning of inventory and logistics management. The importance of collaborative relationship building with HSC partners for developing capability and the effective use of available resources are keys to success. These improved relationships also help to promote postponement and similar speculation-based logistics strategies, as well as advanced purchasing and pre-positioning strategies. Finally, the speed and quality of response is found to be the top enabler in GHSC management.Research limitations/implicationsOne noted shortcoming of the chosen research method is its reliance on subjective expert judgement. However, collecting judgements is at the basis of many research methods, and the research team took utmost care throughout the research process to allay biases. Future empirical research can further examine the relationships suggested herein. Managers can use the model developed in this research to consider impactful ways to design and execute sustainable HSCs.Originality/valueTo the best of the authors' knowledge, this is a novel attempt to identify enablers to GHSC management. Secondly, the research team has used an advanced methodology (fuzzy TISM) to develop the contextual inter-relationships among the enablers which has not been used earlier in this direction before and thus advances the GHSC literature.

Construction Contract Administration Performance Assessment Tool by Using a Fuzzy Structural Equation Model

The contract administration process is inevitably complex, and improper performance of the associated tasks and procedures may lead to disputes between the contracting parties whilst further driving against the sustainability bottom lines. Therefore, this paper proposes an assessment tool to improve the implementation of construction contract administration (CCA) through a multi-dimensional construction contract administration performance model (CAPM), construction contract administration performance index (CCAPI), and a mobile software to assess the CCA performance at the project level by integrating the crisp value of the fuzzy set theory within a second-order confirmatory factor analysis of the structural equation modeling technique. A hybrid mobile application (CAPM) is developed using the Ionic framework which can run either in full model mode or short model mode with 93 and 33 key construction administration factors, respectively. Assessment of sustainability practices in the area of contract administration is a part of the holistic model and CAPM defines 13 major key indicators relevant to social and environmental sustainability while economic sustainability is scattered over the rest of the CAPM indicators. The CAPM is practically implemented in 13 international construction projects and the results reveal that the proposed tool is reasonably captured in the different performance levels of CCA, and we conclude a low level of implementing in risk management. As part of the holistic model, assessment of sustainability practices in the area of contract administration is separately discussed and the study reveals the need to improve the environmental and social sustainability practices in contract administration.

Extended Disturbance Observer-Based Integral Sliding Mode Control for Nonlinear System via T–S Fuzzy Model

In this paper, an extended disturbance observer-based integral sliding-mode control for a nonlinear system is proposed by the Takagi-Sugeno (T-S) fuzzy model approach. The proposed method focuses on two points: First, improving the design flexibility of integral fuzzy sliding-mode control (IFSMC) by transforming a conventional T-S fuzzy model structure into a novel form. Second, expanding a previous disturbance observer-based control (DOBC) concept by introducing a Fourier analysis to deal with the non-periodic form of disturbance. By using the modified form of the fuzzy model, a novel sliding surface combined with estimated disturbance through several fuzzy disturbance observers (FDOB) is suggested. Besides, a sufficient condition for guaranteeing asymptotic stability of the sliding dynamic with H ∞ control performance is proposed by applying a proper Lyapunov function with the linear matrix inequality (LMI) concept. Furthermore, a reachability problem of the proposed sliding surface is also handled by introducing another Lyapunov function. Finally, the effectiveness of the proposed method is demonstrated by comparing previous studies, which focused on robust control techniques, based on the simulation results of an inverted-pendulum system.

Fuzzy TISM and DEMATEL approach to analyze Lean variables in Indian Healthcare Industry

During the last few decades, due to rising competition, healthcare organisations have been trying their best to perform more efficiently by providing the best quality services to the patients. Lean is one of these performance improvement strategies that focus on delivering perfect services to the patients. However, lean implementation usually faces issues like employee resistance, poor follow-ups, and financial constraints, etc. The present research has identified 15 critical variables that affect the lean theory in the healthcare industry based on the review of existing literature with the expert's opinion in the healthcare sector as well as academia also. Fuzzy TISM 'fuzzy total interpretive structural modelling and DEMATEL methodologies' have been used to estimate the relationships among these variables. Results of both the methodologies depict that successful implementation of lean theory in healthcare organisations depend upon 'financial capability and lean training'. These key variables possess high driving power low dependent power.

A Multi-Criteria Decision-Making Model for Evaluating Senior Daycare Center Locations.

Many developed and developing countries are facing an imminent population aging and rapid demographics changing problem. The need of various kinds of eldercare is increasing tremendously. A senior daycare center, very similar to a daycare center for toddlers and preschoolers, can provide the elderly a place to go during daytime and have a more diversified social life. In this research, a senior daycare center location evaluation problem is studied, and a model for facilitating the decision-making of the senior daycare center location is constructed by considering the benefits, opportunities, costs, and risks (BOCR) of the locations. Senior daycare center location evaluation factors are listed first through literature review and interview with experts. These factors are used to construct a network, which is applied to prepare a questionnaire to ask about the influences of a criterion to other criteria. The interrelationships among the criteria are calculated by adopting fuzzy interpretative structural modeling (FISM). Based on the results from the FISM, a fuzzy analytic network process (FANP) questionnaire is given out, and the results are used to determine the priorities of the criteria. In addition, the final ranking of the senior daycare center locations can be obtained. The research results can provide references for prospective senior daycare center providers for making relevant decisions.

Identifying Significant Risks and Analyzing Risk Relationship for Construction PPP Projects in China Using Integrated FISM-MICMAC Approach

To meet the growing demand for public facilities and services, many developing countries, including China, have adopted the concept of public–private partnership (PPP). However, there are many risks in PPP projects. Furthermore, these risks affect each other, which may lead to project failure. However, the existing research on the PPP risk relationship has not gone into sufficient detail. Therefore, in order to fill this literature gap, this study proposes a procedural method to analyze the correlation between PPP risks. Firstly, this study, identifies the risks of construction PPP projects in China by combining the literature review with a case study and interviews. Then, fuzzy interpretative structural modeling (FISM) is used to reflect the relationships between these risks and reveal the failure mechanisms of PPP projects. In addition, based on matrix impact cross-reference multiplication applied to a classification (MICMAC) analysis, the risk is divided into four clusters, according to the driving and dependence power, to show the relationship level of the risk. Finally, the paper compares and discusses the research results with other studies and puts forward some suggestions on PPP risks. The FISM-MICMAC method adopted in this study considers the fuzzy of the PPP risk relationship and improves upon previous studies. In addition, the method of FISM-MICMAC can provide a new risk assessment tool for risk management strategies in the field of construction engineering and management.

Fuzzy Control of Uncertain Positive Markov Jump Fuzzy Systems With Input Constraint.

This article considers the stochastic stability of uncertain positive Markov jump fuzzy systems (PMJFSs). The uncertainties consist of partially unknown membership functions and generally uncertain transition rates (GUTRs). In the GUTR model, each transition rate (TR) is completely unknown or only its estimate value is known. The coexistence of the partially unknown membership functions and GUTRs makes the control problem difficult to handle. Based on the T-S fuzzy scheme, a switching-type controller with input constraint which parallels as the structure of the uncertain fuzzy model is applied to ensure the positivity and stochastic stability of such systems. A linear decoupling strategy is proposed to tackle the coupling problems when designing the controller and further derives the desired controller parameters. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.

A Novel Description on Edge-Regular q-Rung Picture Fuzzy Graphs with Application

Picture fuzzy model is a generalized structure of intuitionistic fuzzy model in the sense that it not only assigns the membership and nonmembership values in the form of orthopair ( μ , ν ) to an element, but it assigns a triplet ( μ , η , ν ) , where η denotes the neutral degree and the difference π = 1 - ( μ + η + ν ) indicates the degree of refusal. The q-rung picture fuzzy set( q -RPFS) provides a wide formal mathematical sketch in which uncertain and vague conceptual phenomenon can be precisely and rigorously studied because of its distinctive quality of vast representation space of acceptable triplets. This paper discusses some properties including edge regularity, total edge regularity and perfect edge regularity of q-rung picture fuzzy graphs (q-RPFGs). The work introduces and investigates these properties for square q-RPFGs and q-RPF line graphs. Furthermore, this study characterizes how regularity and edge regularity of q-RPFGs structurally relate. In addition, it presents the concept of ego-networks to extract knowledge from large social networks under q-rung picture fuzzy environment with algorithm.

Critical barriers to brownfield redevelopment in developing countries: The case of Pakistan

Due to the scarcity of land for urban development, Brownfield Redevelopment (BR) is considered a sustainable urban land tool in developed countries, but its adoption in developing countries remains in its infancy. Barriers impeding BR need to be investigated in detail. However, few studies have analyzed barriers to BR in developing countries. This study aims to analyze the critical barriers to BR in Pakistan by using a two-stage methodology including the Fuzzy Delphi Method (FDM) and Structural Equation Modeling (SEM). Initially, 41 barriers were identified through a comprehensive literature review. FDM screened out 33 barriers through expert opinion. Survey questionnaires were sent to major stakeholders involved in the BR process. Finally, valid responses were analyzed using SEM to verify and rank the most critical barriers. Study results indicated 26 critical barriers. The top five critical barriers were lack of policy incentives, the complexity of public-private partnerships, lack of professional and technical personnel, the conflict between stakeholders, and lack of awareness of environmental law. A comparative analysis of barriers to BR in Pakistan and other countries including the USA, Canada, the UK, Germany, and China showed that critical barriers vary by country. Furthermore, factor analysis results extracted five major categories for 26 critical barriers, including political and legal barriers, financial and economic barriers, technical and operational barriers, management system barriers, and environmental barriers. The most dominant barrier category was technical and operational barriers, impeding BR in Pakistan. This study creates value in the BR literature by identifying the most critical barriers to BR in a developing country. It could support practitioners and land use policy makers in developing appropriate strategies to promote BR.

Energy structure analysis and energy saving of complex chemical industries: A novel fuzzy interpretative structural model

Chemical production plays a key effect in energy saving and the sustainable development. However, the uncertain data has a direct impact on the production and the energy efficiency of complex chemical industries. Therefore, in order to analyze the energy structure and improve the energy efficiency of complex chemical industries, this paper proposes a novel interpretative structural model (ISM) integrated the fuzzy theory (FISM). The production data are divided into the upper limit value, the lower limit value and the most probable value based on the data fuzzification. And then energy structures and energy saving potentials in different production configurations are obtained by using the FISM. Finally, the proposed method is applied to analyze the energy structure and obtain energy saving potentials of the ethylene production process in complex chemical industries. The experimental results show that the key impact factors of influencing the ethylene production in different production configurations can be obtained to improve the energy efficiency. Moreover, compared with the benchmarking of the best production configuration, the raw materials can be saved about 10–30% and the carbon emissions can be reduced about 0.3–0.45 Ton when producing one Ton ethylene.