Intuitionistic Fuzzy System Research Articles

Reliability evaluation of a medical oxygen supply system by FTA based on intuitionistic fuzzy sets

The main objective of healthcare centers is to ensure a consistent and high-purity oxygen supply for the safety of patients. Pressure Swing Adsorption (PSA) is a commonly used technique for supplying medical oxygen by eliminating nitrogen from the ambient air. Due to the increased fire risk associated with oxygen, any malfunctions in the oxygen supply system can lead to serious hospital fires. This study aimed to assess the reliability of the oxygen supply system using Fault Tree Analysis (FTA) based on both the traditional method and Intuitionistic Fuzzy Analysis. FTA is a logical analysis method used to identify events that could result in an undesirable top event and calculate the probability of its occurrence. The FTA of the PSA system outlined the logical sequence of events that could lead to the release of oxygen as a top event. The study revealed that the failure of the pressure switch in the concentrator, inadequate sealing, and the use of inappropriate materials for the seals were the primary basic events that led to the release of oxygen. Additionally, this study utilized the Intuitionistic Fuzzy System to quantify experts' opinions, providing more flexibility in handling ambiguous situations and considering both membership and non-membership functions simultaneously.

EEG Data Analysis Techniques for Precision Removal and Enhanced Alzheimer’s Diagnosis: Focusing on Fuzzy and Intuitionistic Fuzzy Logic Techniques

Effective management of EEG artifacts is pivotal for accurate neurological diagnostics, particularly in detecting early stages of Alzheimer’s disease. This review delves into the cutting-edge domain of fuzzy logic techniques, emphasizing intuitionistic fuzzy systems, which offer refined handling of uncertainties inherent in EEG data. These methods not only enhance artifact identification and removal but also integrate seamlessly with other AI technologies to push the boundaries of EEG analysis. By exploring a range of approaches from standard protocols to advanced machine learning models, this paper provides a comprehensive overview of current strategies and emerging technologies in EEG artifact management. Notably, the fusion of fuzzy logic with neural network models illustrates significant advancements in distinguishing between genuine neurological activity and noise. This synthesis of technologies not only improves diagnostic accuracy but also enriches the toolset available to researchers and clinicians alike, facilitating earlier and more precise identification of neurodegenerative diseases. The review ultimately underscores the transformative potential of integrating diverse computational techniques, setting a new standard in EEG analysis and paving the way for future innovations in medical diagnostics.

Triangular intuitionistic fuzzy linear system of equations with applications: an analytical approach

This study extended an existing semi-analytical technique, the Homotopy Perturbation Method, to the Block Homotopy Modified Perturbation Method by solving two n × n crisp triangular intuitionistic fuzzy (TIF) systems of linear equations. In the original system, the coefficient matrix is considered as real crisp, while the unknown variable vector and right hand side vector are regarded as triangular intuitionistic fuzzy numbers. The Block Homotopy Modified Perturbation Method is found to be efficient and practical to solve n × n TIF linear systems as it only requires the non-singularity of the n × n TIF linear system's coefficient matrix, whereas the point Homotopy Perturbation Method and other classical numerical iterative methods typically require non-zero diagonal entries in the coefficient matrix. A set of theorems relevant to this study are presented and demonstrated. We solve an engineering application, i.e. a current flow circuit problem that is represented in terms of a triangular intuitionistic fuzzy environment, using the suggested method. The unknown current is then obtained as a triangle intuitionistic fuzzy number. The proposed semi-analytic method is used to solve some numerical test problems in order to validate their performance and efficiency in comparison to other existing techniques. The numerical results of the example are displayed on graphs with different degrees of uncertainty. The efficiency and accuracy of the proposed method are further demonstrated by comparisons to block Jacobi, Adomain Decomposition method, Successive Over-Relaxation method and the classical Gauss-Seidel numerical method.

Dynamic risk assessment of hospital oxygen supply system by HAZOP and intuitionistic fuzzy.

Events such as oxygen leakage in the oxygen generation systems can have severe consequences, such as fire and explosion. In addition, the disruption in the oxygenation systems can lead to a threat to patients' lives. Thus, this study aimed to identify the significant deviations in the oxygen supply system as critical equipment at hospitals based on the Hazard and Operability (HAZOP) method. Despite the advantages of risk assessment techniques, hazard identification techniques are still being utilized with deterministic and unreliable values and have a completely static nature. Therefore, using dynamic techniques to overcome intrinsic ambiguity in the risk assessment process through fuzzy sets has been recommended. Additionally, we proposed the HAZOP methodology to integrate with the intuitionistic fuzzy system for assessing the medical oxygen supply system using Pressure Swing Absorbance technology as a proactive approach. The results showed that the intuitionistic fuzzy approach, combined with the risk assessment method, is a suitable tool to eliminate uncertainty, improve decision-making, and result in more detailed and accurate findings. The approach adopted in this study can be used as a needs assessment tool to optimize maintenance programs and provide the necessary training for the staff, maintenance operators, and medical equipment managers.

Semi-Analytical Scheme for Solving Intuitionistic Fuzzy System of Differential Equations

The aim of this article is to implement the Generalized Modified Adomian Decomposition Method to compute the semi-numerical solution of the linear system of intuitionistic fuzzy initial value problems. Here, we consider the initial values as generalized trapezoidal intuitionistic fuzzy numbers. The technique is applied to brine tanks problem and coupled mass spring systems.Theoretically, different approaches to solving a system of generalized trapezoidal intuitionistic fuzzy differential equations are discussed in this study under the presumption that the coefficients of the system of the differential equations are associated to generalized trapezoidal intuitionistic fuzzy numbers. The approximate results are compared with exact solutions which shows good efficiency. The corresponding graphs at different levels of uncertainty show the example’s numerical outcomes. The graphical representations further demonstrate the effectiveness and accuracy of the proposed method in comparison to existing semi-numerical methods in the literature.

Incomplete Complex Intuitionistic Fuzzy System: Preference Relations, Expert Weight Determination, Group Decision-Making and Their Calculation Algorithms

As is well known, complex intuitionistic fuzzy preference relation can describe the fuzzy characters of things in more detail and comprehensively and is very useful in dealing with decision-making problems that include periodic or recurring phenomena. However, sometimes, a decision-maker may provide incomplete judgments in a complex intuitionistic fuzzy preference relation because of a lack of knowledge, time pressure, and the decision-makers’ limited expertise related to the problem domain. In such cases, it would be sensible not to force the expert to express “false” preferences over these objects. Consequently, how to define incomplete complex intuitionistic fuzzy preference relations and to estimate their missing elements in an incomplete complex intuitionistic fuzzy preference relation becomes a necessary step in a decision-making process. In this paper, the concept of incomplete complex intuitionistic fuzzy preference relation is introduced and its properties are discussed. Meanwhile, the multiplicative consistent incomplete complex intuitionistic fuzzy preference relations are defined. Secondly, estimating algorithms are developed to estimate the missing elements in the acceptable incomplete complex intuitionistic fuzzy preference relations. Finally, an expert weight determination algorithm and the group decision-making algorithms based on incomplete complex intuitionistic fuzzy preference relations are established. The solving process of the algorithms is illustrated by an example, the practicability of the algorithms is verified, the advantages and disadvantages of two group decision-making algorithms are compared and analyzed, and the simulation verification of incomplete complex intuitionistic fuzzy system is carried out by MATLAB software. The framework proposed in this paper effectively generalizes and enriches the previous works and has a good application prospect.

Neural intuitionistic fuzzy system with justified granularity

Neural intuitionistic fuzzy system with justified granularity

Entropy of Dynamical Systems on Interval-Valued Intuitionistic Fuzzy Sets

In this work, we introduce the concepts of Shannon entropy and conditional entropy of experiments in the interval-valued intuitionistic fuzzy case, and study the basic properties of the information measures. Subsequently, by means of the suggested notion of entropy of partitions, we define the entropy of a dynamical system on interval-valued intuitionistic fuzzy sets (IVIF). A version of the Kolmogorov–Sinai theorem on generators for dynamical systems on the IVIF is proved. It is shown that this entropy is an invariant under isomorphisms of interval-valued intuitionistic fuzzy dynamical systems; thus, we obtain a tool for distinguishing some non-isomorphic interval-valued intuitionistic fuzzy dynamical systems. The proposed measure can be used as a measure of information of experiment whose outcomes are interval-valued intuitionistic fuzzy events.

Enriched intuitionistic fuzzy rule pruning using butterfly optimization for rule pruning in intrusion detection

The process of detecting and reacting to hostile activities against the computer and network resources is known as intrusion detection. Rather of relying just on expertise and experience, a systematic and automated IDS creation process is required. This drives researchers to look into data mining-based intrusion detection frameworks. Though there are many algorithms are developed for detection intrusion, the issue of inconsistency in rule-based classification model is controlling the volume of data and removing irrelevant rules are the major problems focused in this work to improve the process of IDS. This paper proposed a framework which handles the hesitancy in classifying the unknown traffic pattern data packets by designing evolutionary algorithm optimized intuitionistic fuzzy inference system. The Intuitionistic fuzzy inference system represents each instances of KDD cup 99 dataset with the degree of membership and non-membership. With these two degrees, the hesitancy degree is computed to overcome the issue of inconsistency and uncertainty in analysing the unknown pattern of abnormal packets. In conventional Intuitionistic fuzzy System (IFS), the rules generated by the inference engine is applied for classification directly without determine the validation of it.

A reply to Madera et al.'s "A method for optimizing a bidding strategy for online advertising through the use of intuitionistic fuzzy systems"

In 2016, Madera et al. tested the performance of a fuzzy inference system against the Google Ads algorithms for optimizing the number of clicks, the click through rate (CTR) and the average cost per clicks to lower the cost of an advertising campaign [7]. The results of their experiments suggested that the implementation of their fuzzy inference system outperformed the Google Ads algorithms in terms of the obtained number of clicks and cost per clicks. While the research idea is with no doubts an interesting and valuable contribution to the fields of digital marketing research, in the opinion of the authors, their experimental setup was flawed. However, applying a few adjustments can lead to valid findings. This paper reflects on the flaws and suggests enhancements to correct them.

On Z-Intuitionistic Fuzzy Fractional Valuations for Medical Diagnosis: An Intuitionistic Fuzzy Knowledge-Based Expert System

In an uncertain situation, data may present in continuous form or discrete form. We have various techniques to deal with continuous data in a realistic situation. However, when data are in discrete form, the existing techniques are inadequate to deal with these situations, and these techniques cannot provide the proper modulation for adequate analysis of the system. In order to provide the proper acceleration to discrete data, we need an appropriate modulation technique that can help us to handle unconditional boundedness on the technique and will operate like the techniques used for continuous data with fractional variables. In this work, we developed an intuitionistic fuzzy fractional knowledge-based expert system using unconditional and qualified fuzzy propositions based on the Z-intuitionistic fuzzy fractional valuation probability density function. In this proposed method, the discrete fractional variables will be converted into intuitionistic fuzzy fractional numbers and then be used in our algorithm. The proposed Z-intuitionistic fuzzy fractional valuation knowledge-based system can easily be applied in the medical field for the diagnosis of diseases in a vague environment due to the ordered-pair characteristics of the Z-intuitionistic fuzzy fractional valuation. In this study, we collected data of dengue patients, which included seven clinical findings: Temperature, sugar, Pulse Rate (PR), age, cough, and Blood Pressure (BP). A numerical example was also carried out to elaborate on the present technique. In addition, a comparative study is discussed in this work. We also provide the managerial implications of the data, with the limitations of the proposed technique presented at the end of this work.

A novel top-n recommendation method for multi-criteria collaborative filtering

Most online service providers utilize a recommender system to help their customers’ decision making process by producing referrals. If a customer requests a suggestion for a specific item, the recommender systems produce predictions for it. On the other hand, it is also possible to create top-n lists containing the products that the customer might like the most. Recommender systems’ outcomes depend on individuals’ preferences which can be provided by considering a single criterion or multiple criteria about the services or products. Therefore, there must be methods to produce predictions and top-n lists for single and multiple-criteria datasets. Although the researchers introduced several algorithms on single criterion-based ratings for producing single predictions and top-n lists, there are only methods for producing referrals for a specific item on multi-criteria data. Accordingly, this paper proposes an intuitionistic fuzzy set-based top-n recommender system method with a novel neighborhood formation process for multi-criteria datasets. The proposed method consists of two crucial points: (i) Determining the relational structure between products; (ii) Investigating user tendencies, as well as their distinctive structures and rating distributions. The rating distribution and the relational structure between the products are determined with association rule mining and entropy measure, while the attitudes and tendencies of the users during the evaluation are analyzed with intuitionistic fuzzy sets. We also adopt a single-criterion top-n method to a multi-criteria recommender system, and we employ crisp ratings instead of fuzzy ones to compare the performance of the proposed method. The measurements of serendipity, diversity, and novelty are utilized to show how the experimental results are compelling. When the experiments’ results are examined, it is concluded that our method can generate successful top-n lists.

Optimization of Interval Type-2 Intuitionistic Fuzzy Logic System for Prediction Problems

Derivative-based algorithms have been adopted in the literature for the optimization of membership and non-membership function parameters of interval type-2 (T2) intuitionistic fuzzy logic systems (FLSs). In this study, a non-derivative-based algorithm called sliding mode control learning algorithm is proposed to tune the parameters of interval T2 intuitionistic FLS for the first time. The proposed rule-based learning system employs the Takagi–Sugeno–Kang inference with artificial neural network to pilot the learning process. The new learning system is evaluated using some nonlinear prediction problems. Analyses of results reveal that the proposed learning apparatus outperforms its type-1 version and many existing solutions in the literature and competes favorably with others on the investigated problem instances with low cost in terms of running time.

직관 퍼지 와이블 분포에서 알파 컷 셋을 이용한 시스템 신뢰성 분석

Purpose This study is to analyze system reliability using fuzzy Weibull lifetime distribution in which scale parameters are assumed to be Intuitionistic Fuzzy Set (IFS) with α-cut set due to uncertainties and inaccuracy of data. Methods The IFS is utilized to present the information of membership function and non-membership function with the three intervals that include belief, disbelief and unbelief functions. The paper discusses the intuitionistic fuzzy Weibull system reliability models to assess the membership function and non-membership function by considering the different types of component intuitionistic fuzzy scale parameter. Due to the existence of different types of uncertainties, the three types of component TIFS (Triangular IFS) and TrIFS (Trapezoidal IFS) with α-cut set were presented for computing intuitionistic fuzzy system reliability in both series structure and parallel structure. Results The validity of proposed model is demonstrated by analyzing intuitionistic fuzzy Weibull system reliabilities with α-cut set using different types of intuitionistic fuzzy scale parameter. A numerical example is indicated to discuss the usefulness and practicality of the proposed model. Conclusions When conducting a group decision making for evaluating the intuitionistic fuzzy Weibull system reliability, α-cut set models proposed in this paper can be used to reflect various experts’ judgments on agreement, disagreement and undecidedness when evaluating system reliability.

An intelligent quantitative trading system based on intuitionistic-GRU fuzzy neural networks

Quantitative trading based on intelligent algorithms has been a hot topic in the financial fields. However, the high noises and outliers increase the uncertainty and non-stationary of financial data such that intelligent algorithms often cease to be effective in the application scenario. Therefore, how to reduce the influence of uncertainty and extract the main trends of financial data is meaningful for trading. Motivated by it, in this article, a novel trading system based on intuitionistic fuzzy neural networks with gated recurrent unit (GRU) is proposed. Firstly, empirical mode decomposition (EMD) is applied to preprocess the original data and obtain the main trends of financial time series on the basis of smoothing data. Secondly, in order to tackle with uncertainty of financial data, a novel interval type-2 intuitionistic fuzzy system (IT2IFS) is proposed for the reasoning process, where hesitancydegree is involved for the learning processes In view of the strong dependence on the time dimension of series data, the gated recurrent unit is integrated into the reasoning model to strengthen the temporal connection. Thirdly, by using the above IT2IFS-GRU model, the quantitative trading system with a concise trading strategy is constructed. By using commodity futures and foreign exchange data, the superior trading results including net profits and precisions, etc. can be obtained, which verifies the effectiveness and availability of the proposed model.

Time-dependent intuitionistic fuzzy system reliability analysis

The purpose of the present paper is to provide a method for constructing time-dependent reliability systems based on intuitionistic fuzzy random variables. A lifetime variable cannot be precisely recorded due to machine errors, experimentation pitfalls, personal judgments, estimation errors or other unexpected sources of error. In order to satisfy the purpose of this paper, an intuitionistic fuzzy random variable with exact parameters was introduced and adopted to evaluate the reliability functions of a k-out-of-n system, with some reliability evaluation criteria discussed and interpreted. Numerical evaluations were further presented to illustrate the calculation of the system reliability criteria in the form of intuitionistic fuzzy numbers. Finally, a number of potential engineering applications of the proposed method were presented.

Interval Type-2 Intuitionistic Fuzzy Logic System for Time Series and Identification Problems - A Comparative Study

This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models.

A New Representation of Intuitionistic Fuzzy Systems and Their Applications in Critical Decision Making

In decision-making problem, fuzzy system is considered as an effective tool with access to uncertain information by fuzzy representations. Evolutionary fuzzy systems have been developed with the appearance of intuitionistic fuzzy, hesitant fuzzy, neutrosophic representations, etc. Moreover, by capturing compound features and convey multifaceted information, complex numbers are utilized to generalize fuzzy and intuitionistic fuzzy sets. However, the order relations established in these existing systems have certain limitations, such as they are not total order relations or they are defined based on intermediate functions, hence it is difficult to use in building and ensuring important properties of logical operators and distance measures in the systems. In this article, a representation of the intuitionistic fuzzy systems based on complex numbers (IFS-C) in the polar form by a new way is proposed to overcome the above restrictions. Specifically, an intuitionistic fuzzy set is characterized by the two functions of modulus and argument. A new order relation, set-theoretic operations, and a new distance measure by the polar form of IFS-C are defined and investigated. The applicability of the proposal is illustrated by a new decision-making model called P-distance measure. It is tested on the benchmark medical datasets in comparison with the existing methods. The experiments confirm the advantages of the proposal.

Comparing Performance Potentials of Classical and Intuitionistic Fuzzy Systems in Terms of Sculpting the State Space

This article provides new application-independent perspectives about the performance potential of an intuitionistic (I-) fuzzy system over a (classical) Takagi-Sugeno-Kang (TSK) fuzzy system. It does this by extending sculpting the state-space works from a TSK fuzzy system to an I-fuzzy system. It demonstrates that, for piecewise-linear membership functions (trapezoids and triangles), an I-fuzzy system always has significantly more first-order rule partitions of the state space-the coarse sculpting of the state space-than does a TSK fuzzy system, and that some I-fuzzy systems also have more second-order rule partitions of the state space-the fine sculpting of the state space-than does a TSK fuzzy system. It is the author's conjecture that for piecewise-linear membership functions (trapezoids and triangles): it is the always significantly greater coarse (and possibly fine) sculpting of the state space that provides an I-fuzzy system with the potential to outperform a TSK fuzzy system, and that a type-1 I-fuzzy system has the potential to outperform an interval type-2 fuzzy system.

Intuitionistic fuzzy adaptive sliding mode control of nonlinear systems

In this paper, Takagi–Sugeno intuitionistic fuzzy adaptive sliding mode control system (TS-IFASMC) is designed for nonlinear systems. We propose an intuitionistic fuzzy method to determine the parameters of the adaptive sliding mode control method which is used to control nonlinear systems. Intuitionistic fuzzy systems are considered as a skilled tool to model uncertainty in systems so they can transfer expert knowledge to control schemes better than the other classical methods, and real-world problems can be handled more effectively with this control method. In the proposed system, control parameters are defined by the intuitionistic fuzzy membership, non-membership, hesitation degrees and an integral sliding mode surface for a robust control performance. The novelty of this study is the use of Takagi–Sugeno type intuitionistic fuzzy system in adaptive sliding mode control method and comparison of performance of this new system with other classical methods. Thus, adaptive sliding mode controller based on the Takagi–Sugeno intuitionistic fuzzy system is obtained to provide robust control performance. Finally, the results support the effectiveness of the presented control scheme.