Fuzzy Concept Research Articles

A novel TOPSIS method with decision-theoretic rough fuzzy sets

The TOPSIS method follows a basic principle, that is, the better the alternative, the farther the alternative is to the pessimistic ideal solution, and the closer the alternative is to the optimistic ideal solution. However, in the decision-making process, there may be some alternatives whose ranking cannot be directly judged from this perspective. Therefore, researchers have proposed some sorting formulas, which can accomplish the overall sorting of alternatives. But, these formulas can only be used to deal with the sorting of alternatives. To expand the application scope and decision prospect of the TOPSIS method, we introduce decision-theoretic rough fuzzy sets, and design a novel TOPSIS method that simultaneously deals with the sorting and classification of alternatives. Using the basic principle of the TOPSIS method to acquire the optimistic and pessimistic ideal distances, we regard these two distances as two fuzzy concepts, here their features are opposite, and utilize the newly defined decision-theoretic rough fuzzy set models to obtain the corresponding decision areas. Then, according to these two distances, we analyze connection between the ordering of alternatives and the total loss of alternatives and summarize the sorting rules when alternatives are classified into the same decision area and the sorting rules when alternatives are classified into different decision areas. Further, the concept of joint decision area is defined. Meanwhile, we summarize the sorting rules when alternatives are classified into the same joint decision area and the sorting rules when alternatives are classified into different joint decision areas. Using these rules, we introduce the detailed process of the novel TOPSIS method and analyze the rationality of the method. Subsequently, we test the feasibility of our method with project investment decision example and illustrate its superiority and stability through comparative analysis and parameter test. Besides, we conduct simulation experiment to further verify the similarities and differences of the sorting function of the traditional TOPSIS method and our method.

Fuzzy formal concept analysis: approaches, applications and issues

Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

Fuzzy formal concept analysis: approaches, applications and issues

Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

This paper proposes a multi-objective teaching–learning studying-based algorithm (MTLSBA) to handle different objective frameworks for solving the large-scale Combined Heat and Power Economic Environmental Dispatch (CHPEED) problem. It aims at minimizing the fuel costs and emissions by managing the power-only, CHP and heat-only units. TLSBA is a modified version of TLBA to increase its global optimization performance by merging a new studying strategy. Based on this integrated tactic, every participant gathers knowledge from someone else randomly to improve his position. The position is specified as the vector of the design variables, which are the power and heat outputs from the power-only, CHP and heat-only units. TLSBA has been upgraded to include an extra Pareto archiving to capture and sustain the non-dominated responses. The objective characteristic is dynamically adapted by systematically modifying the shape of the applicable objective model. Likewise, a decision-making approach based on the fuzzy concept is used to select the most suitable CHPEED solution for large-scale dispatching of combined electrical power and heat energies. The proposed MTLSBA is assigned to multiple testing of 5-unit, 7-unit and 96-unit systems. It is contrasted with other reported techniques in the literature. According to numerical data, the suggested MTLSBA outperforms the others in terms of effectiveness and robustness indices. For the 5-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 0.6625% and 0.3677% and reduction in the emissions of 2.723% and 7.4669% compared to non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA 2), respectively. For the 7-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 2.927% and 3.041% and reduction in the emissions of 40.156% and 40.050% compared to NSGA-II and SPEA 2, respectively.

Design of a fuzzy-based proportional integral derivative controller with optimal membership function scaling for respiratory ventilation system

In this paper, an optimum membership function based fuzzy proportional integral derivative (Fuzzy-PID) controller is proposed to improve the pressure tracking profile of pressure controlled ventilator (PCV) system. A PCV structure as a respiratory support system is spatially designed for seated patients with homogenous lung system. During anesthesia procedure, such type of ventilator is used. For the comfort and safety of patients, the airway pressure of such device must exactly track the desired pressure of patients. As, PID based control structure is well accepted in industry due to the simple structure, easy implementation and efficiency to work robustly. Therefore, a Fuzzy-PID control structure is considered for PCV system. The fuzzy concept is used to tune the parameters of the PID controller. The novelty of the present work is that the membership function of fuzzy inputs (error and change in error in terms of airway pressure) and the outputs (parameters of the PID controller) are optimized with a swarm based optimizer. Therefore, a boosted class topper optimization (BCTO) algorithm is developed. With the proposed control scheme, the presser tracking profile of PCV system is improved in terms of response time, settling time and overshoot and compared with existing one.

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

BackgroundIn times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.ResultsWe argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.ConclusionsWe conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.

A novel probabilistic hesitant fuzzy rough set based multi-criteria decision-making method

In this study, the fusion of probabilistic hesitant fuzzy sets (PHFSs) and rough sets is explored and applied to uncertain multi-criteria decision-making (MCDM). First, to reduce the huge amount of computation of probabilistic hesitant fuzzy operations in the MCDM process, we introduce an advanced method to obtain normalized PHFSs (NPHFSs), in which each probabilistic hesitant fuzzy element (PHFE) has the same length and probability distribution. Second, based on NPHFSs, a novel probabilistic hesitant fuzzy rough set (PHFRS) model is established to solve the information aggregation problem in MCDM. Subsequently, we propose the concept of fuzziness of a PHFRS and develop a fuzziness-based objective weight determination method. Further, we construct an extended PROMETHEE II method based on a PHFRS for MCDM problems. Finally, we analyze the effectiveness of our proposed method through two MCDM problems along with experimental comparisons. The results illustrate that the proposed method is useful in numerous uncertain application fields.

Reliability Analysis of a Functional Diagnostic Test for Primary Hyperaldosteronism Based on Data Analysis.

Primary aldosteronism (PA) is one of the most common causes of secondary hypertension, with a prevalence of 12–20% in the hypertensive population. To determine the characteristic function of a fuzzy concept based on the epidemiological data, clinical manifestations, and auxiliary examinations of PA, the essence is to select a suitable domain and determine the affiliation of each element in the domain. The aldosterone/renin ratio was proposed to increase the detection rate of PA, which has the shortcoming of a high underdiagnosis rate when relying only on clinical manifestations. However, there is no unified standard for the diagnostic cut point, and there are differences in testing methods and diagnostic cut point values for different populations, which require different laboratories to establish appropriate cut points according to different regional populations to improve the diagnostic accuracy. In this article, we analyzed the reliability of functional diagnostic tests for PA based on data analysis and compared the sensitivity and specificity of different plasma aldosterone cut points for the diagnosis of PA in the 40 mg kibbutz test. The results showed that when post-saline PAC and post-cato PAC were used to confirm the diagnosis of proaldosterone, respectively, there was a similar subject working area under the curve between SSST and CCT, 0.89 and 0.78, respectively, with no significant difference in the area under the curve between the two (p=0.546). Therefore, blood sodium and blood potassium have higher specificity and sensitivity than SUSPUP, but both are lower than ARR, and data analysis can be used as an auxiliary indicator for screening.

MODEL LOGISTIK FUZZY DENGAN ADANYA PEMANENAN PROPORSIONAL

The logistic growth model with proportional harvesting is a population growth model that takes into account harvesting factors. In real life, not all conditions can be known with certainty, such as different growth rates in each population and harvest rates depending on the needs of the harvester. To overcome these conditions, there is a concept that accommodates the problem of uncertainty, namely the fuzzy concept. This concept can be induced into a logistic model with proportional harvesting which assumes the intrinsic growth rate and the harvest rate is expressed by fuzzy numbers. The purpose of this research is to form a logistic model with fuzzy proportional harvesting, analyze the stability of the model, and form a numerical simulation. This study uses the alpha-cut method to generalize the intrinsic growth rate and harvest rate from crisp numbers to fuzzy numbers, then the Graded Mean Integration Representation (GMIR) method to defuzzify the model, and the linearization method to analyze the stability of the model. The results of this study obtained a logistic model with proportional harvesting. Then the model was developed into a logistic model with fuzzy proportional harvesting by assuming the intrinsic growth rate and the harvest rate expressed by fuzzy numbers. From the model obtained 2 equilibrium points, namely the first equilibrium point is unstable and the second equilibrium point is asymptotically stable under certain conditions. Model simulation is given to show illustration of stability analysis. From the simulation, it can also be shown that the higher the graded mean value, the lower the population.

Fuzzy logic concept based MPPT technique for standalone PV system

This paper presents a study of an Intelligent Maximum Power Point Tracking (IMPPT) system for the Standalone PV system. The proposed MPPT technique is based on a fuzzy control concept (FLC). FLC is easy to design due it doesn’t require precise system knowledge and work well with the nonlinear systems; the obtained results were compared with the famous MPPT method (P&O). According to the obtained simulation results, the proposed method gave good results compared to the P&O technique, these results are shown by the low oscillation and rapid response of the proposed method, which provides the maximum transmission of power to the load.

Stages in Theory and Experiment. Fuzzy-Structuralism and Piagetian Stages

The theory of stages in cognitive development is one of Jean Piaget’s enduring legacies, but it has also borne the brunt of much criticism. It maintains that intelligence develops in an invariant sequence of stages, and, in this paper, I situate Piaget’s conceptions of stages historically and functionally in the context of genetic epistemology, his research programme. I highlight some of the objections raised, and I show how the disparity between the conceptions of theoretical and empirical stages in Piaget’s theory is commensurate with the fuzzy-structuralist model of the relationship between theory and empirical research conceived by Rudolf Seising on the basis of Lofti A. Zadeh’s fuzzy set theory. Further, I propose a fuzzy conception of the notion ‘stage’, which not only captures its ordinary use in fuzzy space between theory and empirical research but also does justice to both the construct validity and quantitative variability of stages in empirical research. I therefore open a fuzzy-structuralist perspective on the Crisis of Variability afflicting Piaget’s stage theory during the 1970s and conclude retrospectively that the rift it caused was not necessary since the invariance and variability of stages is not irreconcilable.

Primary interval-valued intuitionistic fuzzy M group

The concept of interval-valued intuitionistic fuzzy M group is extended by introducing primary interval-valued intuitionistic fuzzy M group and primary interval-valued intuitionistic fuzzy anti M group using this concept primary interval-valued intuitionistic fuzzy M group and primary interval-valued intuitionistic fuzzy anti M group is defined and using some properties are established.

Analysis of Fuzzy Kuramoto-Sivashinsky Equations under a Generalized Fuzzy Fractional Derivative Operator

This paper evaluates a semianalytical strategy combined with a novel fuzzy integral transformation and an iterative method inside the fuzziness concept known as the new iterative transform method. Additionally, we apply the abovementioned technique to the fractional fuzzy Kuramoto-Sivashinsky equations with g H -differentiability by employing various initial conditions. Numerous algebraic properties of the fuzzy fractional derivative Atangana-Baleanu operator are illustrated concerning the Shehu transformation to demonstrate their utility. Additionally, a general technique for Atangana-Baleanu fuzzy fractional derivatives is proposed in the sense of Caputo. It is important to note that the purpose of the suggested fuzziness technique is to establish the efficiency and accuracy of analytical solution to nonlinear fuzzy fractional partial differential equations that emerge in complex and physical structures.

Integrating Deterministic and Fuzzy Concepts into the Benefit–Cost Analysis of Wyoming’s Proposed Pavement Testing Track Facility

Integrating Deterministic and Fuzzy Concepts into the Benefit–Cost Analysis of Wyoming’s Proposed Pavement Testing Track Facility

A Fuzzy Imperfect Production Inventory Model Based on Fuzzy Differential and Fuzzy Integral Method

In the inventory theory, to treat the uncertainty, the fuzzy set concept is used in order to provide a feasible approach to deal with the uncertainty problem. In this research work, a fuzzy economic production quantity model with interactive fuzzy demands is proposed. In a production process, in the beginning, the system is assumed to be in a controlled state in which only perfect items are manufactured. Later, the manufacturing production process shifts to be an out-of-control-state system; producing both perfect and imperfect items simultaneously, this is considered as a fuzzy state. The defective production rate is also taken into account as a fuzzy state. Here, the selection process of produced items is realized during the production period. With the aim of studying the practical feasibility of the fuzzy economic production inventory model along with a sensitivity analysis of some parameters, different numerical examples are illustrated.

Fuzzy risk-based framework for scheduling of energy storage systems in photovoltaic-rich networks

Recently, the need to generate electricity with less carbon dioxide emissions has resulted in a significant integration of renewable energy sources (RESs) into the distribution networks. However, the electric power generated by many RESs, such as wind power and solar power, are intermittent. To compensate for this issue, energy storage systems (ESSs) are utilized as a potential alternative. For appropriate integration of ESSs and RESs in active distribution networks, optimization tools are indispensable. In this paper a risk-based framework is explored to optimize day-to-day charging/discharge schedule of ESS embedded photovoltaic (PV) and diesel generators in interconnected microgrids. Two primary objective functions, including the capital expenditure, and the Voltage Stability Factor (VSF) are developed and then simultaneously optimized through Multi-Objective Teaching Learning Based Optimization algorithm, which gives a set of solutions instead of a single global solution. Three final solutions are then chosen from the point of view of risk-averse, risk-neutral and risk-seeker operators among a set of solutions via a fuzzy concept. Moreover, due to integration of RERs in the system, an effective and time-efficient classification is utilized for modeling the uncertainty of such resources. Finally, Profitability and feasibility of the proposed approach are identified by implementing it on a practical 51-bus system that is exploited as the interconnected microgrids under various case studies.

Moving Target Detection Using Fuzzy Bayesian Fusion in Multichannel SAR Framework

In this paper, a novel FBF-MTD is proposed for the detection of moving target by integrating the fuzzy concept in Bayesian fusion model. This method uses the decision fusion method that combines the matching filter, Fourier transform and the STFT. In the first step, acceleration, velocity, and RCS are simulated and the radar that returns from the target is calculated based on transmission power, distance of target, antenna gain, and RCS. Then, the FBF-MTD method combines the results of Fourier transform, short time Fourier transform, and matched filter, to produce the final decision. The performance of the proposed FBF-MTD method is analyzed with respect to the metrics, namely detection time, missed target rate, and MSE. The proposed FBF-MTD model obtained the detection time, missed target rate, and MSE values of 3.2495 sec, 0.0524, and 3344.04, respectively that show the superiority of the FBF-MTD model in MTD.

Series Type Solution of Fuzzy Fractional Order Swift–Hohenberg Equation by Fuzzy Hybrid Sumudu Transform

There are some confusion and complexity in our everyday lives, as we live in an uncertain environment. In such type of environment, an accurate calculation of the data and finding a solution to a problem is not an easy job. So, fuzzy differential equations are the better tools to model problems in the fuzzy domain. Modeling the real-world phenomenon more accurately requires such operators. Therefore, we investigate the fractional-order Swift–Hohenberg equation in the fuzzy concept. We study this equation under the fuzzy Caputo fractional derivative. We use the fuzzy Sumudu transform to find out the semianalytical solution of the considered equation. To deal with the nonlinear term of the problem, we also use the Adomian decomposition method. To confirm the accuracy of the proposed procedure, we give two test problems. Lastly, we plot the numerical results for various fractional orders, and uncertainty belongs to [0, 1].

Proving Fixed-Point Theorems Employing Fuzzy (σ,𝒵)-Contractive-Type Mappings

In this article, the concept of fuzzy (σ,Z)-contractive mappings is introduced in the setting of fuzzy metric spaces. Thereafter, we utilize our newly introduced concept to prove some existence and uniqueness theorems in M-complete fuzzy metric spaces. Our obtained theorems extend and generalize the corresponding results in the existing literature. Moreover, some examples are adopted to exhibit the utility of the newly obtained results.

DEroute: trust-aware data routing protocol based on encryption and fuzzy concept for MANET secure communication in Iot

ABSTRACT The Internet of Things in smart environments interrelates with Mobile Ad‐hoc Network and Wireless Sensor Network, which becomes more attractive and efficiently flourishing. Moreover, Mobile Ad‐hoc Network is applied in various fields including intelligent transportation, emergency communications after disaster, and Internet of Things. The incorporation of Internet of Things and Mobile Ad‐hoc Network become a rising theory for enabling opportunistic communication in Internet of Things. The security provisioning for secure communication is still the major challenge in Mobile Ad‐hoc Network – Internet of Things. Thus, in this paper, the DEroute algorithm is developed for secure communication Internet of Things structure, where Internet of Things nodes are located in mobile ad-hoc manner. In order to avoid the route list modification, nodes’ address is encrypted based on generated secret keys by Diffie–Hellman key-exchange algorithm. The fuzzy system with trust parameters, like historical, indirect and direct trust factors are considered for the identification of secure nodes. The bi-filtering process is also performed for filtering important nodes. Thus, the developed DEroute model achieved enhanced performance with respect to delay, packet delivery ratio and throughput of 0.1674 sec, 87.23% and 25.19 mbps, respectively.