Type-2 Fuzzy Research Articles

A new intuitionistic fuzzy scheme of data envelopment analysis for evaluating rural comprehensive health service centers

In this study, the classical CCR-IO DEA model (an input-oriented form of the DEA model of Charnes, Cooper, and Rhodes [24] is extended to an intuitionistic fuzzy environment. For this aim, the trapezoidal intuitionistic fuzzy numbers are used to show the values of inputs and outputs. An intuitionistic fuzzy number is characterized by membership, non-membership, and hesitancy degrees. Therefore, such type of fuzzy numbers is more complete and contains more information to reflect a fuzzy type of uncertainty comparing to the conventional fuzzy values. In order to solve the proposed intuitionistic fuzzy CCR-IO model, for the first time a possibility-based approach is developed for the intuitionistic fuzzy DEA models. In this approach for the first time the ME measure of fuzzy events is extended to intuitionistic fuzzy DEA models, therefore, some new definitions and propositions are presented and clarified. This measure is more flexible and effective comparing to the other measures such as credibility, necessity, and possibility measures. A case study from the health care sector of Iran is considered to evaluate the proposed solution approach numerically. The case study is solved under different scenarios, a full sensitivity analysis is performed, and finally some useful managerial insights are presented. In addition, the results of the proposed intuitionistic CCR-IO model are compared to the classical models of the literature.

Annular finite-time stability for IT2 fuzzy networked switched system via non-fragile AETS under multiple attacks: Application to tank reactor chemical process model

This study examines the issue of annular finite-time H∞ performance for interval type-2 fuzzy (IT2) fuzzy networked switched systems vulnerable to multiple attacks via non-fragile adaptive event-triggered control. An adaptive event-triggered scheme (AETS) is presented to minimize the impact of multiple attacks and it effectively reduces redundant data transmission while guaranteeing reliable system performance which differs from the traditional event-triggered scheme. We describe the impact of deception and reply attacks by introducing two random variables that satisfy the Bernoulli distribution. Furthermore, IT2 fuzzy networked switched systems were developed to demonstrate the effects of multiple attacks, and the IT2 fuzzy model describes the nonlinear characteristics. Also, a delay-dependent Lyapunov functional with a free-weighting matrix technique are used. There are limited study findings on the class of IT2 fuzzy networked switching systems using adaptive event-triggered scheme. This paper discusses the advantages of establishing an efficient adaptive event-triggered mechanism for IT2 fuzzy networked switching systems based on previous discussions and necessity. Furthermore, some sufficient conditions that are required to ensure that the system is annular finite-time stable with the stated H∞ performance index is presented along with two examples to demonstrate the suggested control design methodology.

A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.

A Type-1 fuzzy framework based on SVD-QR for predicting the strength of tablets produced using Consigma-25 continuous tablet production plant

In this research paper, the tablet strength as an indication of tabletability and tablet quality was examined and modelled as a function of different process parameters. The effects of the different process parameters of the various units of ConsiGmaTM −25 (i.e., wet granulation, drying, milling, blending and tabletting) were statistically examined. Then, a type-1 fuzzy based modelling framework based on the singular value decomposition-QR (SVD-QR) approach was developed and trained to mathematically map these parameters to the tablet strength. This structure was developed in a way that allows users to predict the tablet strength for tablets produced using different process parameters. Based on the results obtained, the statistical analyses show significant effects of the process parameters on the tablet quality. Furthermore, the proposed framework developed can be used to anticipate the tablet's strength successfully, where the performance measures in terms of the coefficient of determination and mean square error are 0.86 and 0.066, respectively. In addition, it can also provide users with linguistic understanding that can help in understanding as well as controlling the line.

Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration

The rapid integration of renewable energy sources (RES) and the electrification of transportation have significantly transformed modern energy infrastructures, emphasizing the need for efficient and flexible energy management systems. This study presents an intelligent, variable-fed, Type-2 Fuzzy Logic Controller (IT2FLC) designed for optimal management of Hybrid Microgrid (HMG) energy systems, specifically considering different modes of Electric Vehicles (EVs) integration. The necessity of this study arises from the challenges posed by fluctuating renewable energy outputs and the uncoordinated charging practices of EVs, which can lead to grid instability and increased operational costs. The proposed IT2FLC is based on comprehensive mathematical modeling that captures complex interactions among HMG components, including Doubly Fed Induction Generator (DFIG) units, photovoltaic (PV) systems, utility AC power, and EV batteries. Utilizing a yearly dataset for simulation, this work examines the HMG’s flexibility and adaptability under dynamic conditions managed by the proposed intelligent controller. A Simulink-based model is built for this study to replicate the dynamical operation of the HMG and test the precise and real-time decision-making capability of the proposed IT2FLC. The results demonstrate the IT2FLC’s superior performance, achieving a substantial cost avoidance of nearly $3,750,000 and efficient energy balance, affirming its potential to sustain optimal energy utilization under stochastic conditions. Additionally, the results attest that the proposed IT2FLC significantly enhances the resilience and economic feasibility of hybrid microgrids, achieving a balanced energy exchange with the utility grid and efficient utilization of EV batteries, proving to be a superior solution for optimal operation of hybrid grids.

Two-level balancing multi-objective algorithm for trapezoidal type-2 fuzzy flexible job shop problems

Uncertainty remains a critical issue in realistic applications in many fields. However, there is little literature considering trapezoidal interval type-2 fuzzy set (TIT2FS) in scheduling problems. To fill this gap, we propose a two-level balancing multi-objective optimization algorithm for a distributed flexible job shop scheduling problem (DFJSP), wherein two typical constraints are considered: the TIT2FS processing time and the transportation resource limitation. Two objectives are to be minimized: the TIT2FS fuzzy makespan and energy consumption. A two-level evolution mechanism is employed. The first level comprises two populations to optimize the two respective objectives, while the second level is composed of two populations to perform the convergence and diversity balancing tasks. Next, an efficient problem-specific initialization method with several heuristics is presented. Subsequently, different types of mutation and crossover operators are proposed under the consideration of problem knowledge. To further improve the performance of exploration and exploitation abilities, we present a balancing enhanced local search method. Finally, we performed detailed comparisons with four efficient algorithms. The results show that the two-level optimization algorithm was efficient in both convergence and diversity abilities, indicating that our algorithm can efficiently achieve a balance between these abilities.

Evaluation of carbon capture technologies in the oil and gas industry using a socio-technical systems perspective-based decision support system under interval type-2 trapezoidal fuzzy set

Concerns in relation to consequences of global warming and climate change have activated worldwide attempts for mitigating the concentration of carbon dioxide (CO2) produced by the industrial sector. Decarbonizing the oil and gas refining (OGR) industries is a challenging problem for policy-makers owing to its potential to prevent economic, environmental, and health risks. In this regard, CO2 capture, utilization, and storage (CCUS) technologies are the most encouraging options to decarbonize. The technologies related to the part of CO2 capture can play a vital role in solving the mentioned problem. Various technologies have been employed for CO2 capture, and choosing the appropriate technology is a complex multi-criteria decision-making (MCDM) issue. This work develops a novel and robust decision support system (DSS). The DSS integrates MCDM techniques of the Delphi and Entropy integration method (DAEIM) and complex proportional assessment of alternatives (COPRAS) method with the interval type-2 trapezoidal fuzzy (IT2TF) environment. The proposed DSS is used to evaluate, prioritize, and choose technologies for CO2 capture. A hybrid criteria system, which involves elements of socio-technical systems perspective has been used for evaluating the candidate technologies. For implementing the DSS of this work, five capture technologies of post-combustion (A_cc1), pre-combustion (A_cc2), oxy-fuel combustion (A_cc3), direct air capture (A_cc4), and indirect air capture (A_cc5) have been chosen for evaluation. The final value of each technology is A_cc1 (0. 2907), A_cc2 (0.2602), A_cc3 (0.1005), A_cc4 (0.2304), and A_cc5 (0.1181) and the preferences of the technologies are A_cc1> A_cc2> A_cc4> A_cc5> A_cc3. The evaluation findings reveal that post-combustion technology with the value of 0.2907 is the most suitable scenario for the capture of CO2 emissions from Iran's OGR systems. The computation results demonstrate that the suggested DSS is feasible and applicable and give reliable and robust findings for acquiring the optimal CO2 capture technology.

Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network

This study examines the use of injection moulding to evaluate mechanical properties in plastic products, such as shear and residual stresses. Key process variables like melt temperature, mould temperature, hold pressure duration, and pure hold duration are meticulously chosen for study. A full factorial experiment design is utilised to determine the best settings. These variables notably influence the end product’s physical and mechanical properties. Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. A CAD model of a dashboard part is incorporated into a finite element analysis to measure shear and residual stresses. Four specific parameters from the injection moulding process are subjected to an in-depth experimental design. It is worth noting that the injection moulding process does not incorporate a type-2 fuzzy neural network (T2FNN). However, in this particular investigation, T2FNN was employed to replicate the mechanical stress model associated with dashboard injection moulding. Its purpose was to estimate shear and residual stress levels. Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. The proposed model was developed and implemented using MATLAB software. A Pareto front was derived from the MOGA by employing the T2FNN within the process, identifying fourteen optimal solutions.

INT-FUP: Intuitionistic Fuzzy Pooling

Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments.

Type-2 Fuzzy ANP and TOPSIS methods based on trapezoid Fuzzy number with a new metric

Type-2 Fuzzy ANP and TOPSIS methods based on trapezoid Fuzzy number with a new metric

A Novel Interval Type-2 Fuzzy Logic Controller for Shunt Active Power Filters in Power Quality Enhancement

This paper proposes a novel control strategy for enhancing power quality through the implementation of an Interval Type-2 Fuzzy Logic Controller (T2FLC) for Shunt Active Power Filters (SAPF). The presented method effectively addresses harmonic distortions introduced by non-linear loads, a common issue in modern electrical systems. Extensive simulations conducted in MATLAB/Simulink demonstrate the T2FLC's ability to regulate the DC bus voltage while significantly reducing Total Harmonic Distortion (THD). The method achieves superior performance compared to both traditional passive and active filtering techniques, reducing THD within the limits specified by IEEE 519-2014 standards. The results confirm the robustness and adaptability of the proposed approach across varying load conditions, establishing it as a reliable and scalable solution for improving power quality in complex electrical networks.

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

A New Hybrid Approach for Clustering, Classification, and Prediction of World Development Indicators Combining General Type-2 Fuzzy Systems and Neural Networks

FUZZY APPLICATION (MAMDANI METHOD) IN DECISION-MAKING ON LED TV SELECTION

Science is now developing very quickly. Information technology has been used in various places. Using computers in business, government, and personal activities shows how important science and technology are in helping human activities. One method used to solve various problems is fuzzy logic. Several types of Fuzzy are classified as Fuzzy Inference Systems (FIS), namely Tsukamoto, Mamdani, and Sugeno. The application of vague logic in making decisions about choosing an LED TV is to make it easier to select electronic media. This research aims to help people who need clarification on the many LED TV choices currently available. So, we need a decision-making method to help people choose an LED TV that suits their needs and budget. One of the methods used in this research is the Mamdani method. There were 50 LED TV brands in this research, and the criteria used in selecting LED TVs were based on size, resolution, and price. An LED TV that meets the medium size, high resolution, and normal price criteria will be purchased. The LED TV data that meets the medium size, high resolution, and normal price criteria is the Samsung UA43AU7000KXXD brand LED TV. However, the actual decision remains based on the buyer's decision.

Fuzzy Control Systems for Power Quality Improvement—A Systematic Review Exploring Their Efficacy and Efficiency

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control.

A nonlinear disturbance observer for sliding mode control of surge in centrifugal compressors via TCV actuator

Surge is a form of dynamic instability created as an unstable pattern in the flow of fluid and can severely affect centrifugal compressor performance by causing fluctuations in flow and pressure parameters. Due to the heavy and costly damage that the surge may cause in various industrial processes such as petrochemical plants, it is necessary to design an appropriate control system to reduce the effect of this phenomenon. The problem of active surge control of a centrifugal compressor using the throttle control valve (TCV) in the presence of compressor parametric uncertainties as well as large demands on upstream and downstream loads is investigated in this work. The control objective was to design a robust control system that can stabilize the compressor over a wide operating range without knowing the upper bound for the uncertainties and load demand. The controller should also react quickly by generating a smooth control signal without saturating the control input. These objectives are achieved by designing a sliding mode controller along with a nonlinear disturbance observer. The performance of the proposed disturbance observer-based controller is evaluated under various operational and load conditions and the results are compared against fuzzy type 1, conventional sliding mode, and wavelet-based neural network robust adaptive controllers. The results show that the proposed method can tolerate large disturbances without any knowledge on the upper bound of the incident disturbance, both on the downstream pressure and upstream mass flow which is highly desirable in practice. The comparative study proves the efficacy of the proposed method using various performance measures. The study also confirms the superior robust performance and stability of the proposed method in front of matched and mismatched disturbances as well as model uncertainties especially close to the instability boundary. Although choosing a TCV actuator has made the control system design easier, the sensitivity of the control valve to flow coefficient and zero calibration under different operating ranges of the compression system is studied carefully and some recommendations for the users are provided.

Knowledge reduction by combining interval Type-2 Fuzzy similarity measures and interval Type-2 Fuzzy formal lattice

Knowledge reduction by combining interval Type-2 Fuzzy similarity measures and interval Type-2 Fuzzy formal lattice

Interval type-2 fuzzy approach for retinopathy detection in fundus images

In the manuscript, an automatic approach for analysis and detection of various stages of retinopathy defects in human eyes has been proposed. The approach consists of a robust preprocessing technique of the retina fundus image to mitigate the effects of noise and poor lightening in the image. To realize a compressive analysis of the defects, methods for extracting blood vessels and optic disc in the fundus image has also been developed. Adaptive Histogram Equalization (AHE), median filtering and Connected Component Analysis techniques were used in separating blood vessels and optic disc from each fundus image. The pre-processing utilizes canny edge detection and Morphological Closing on the fundus image. An interval type-2 fuzzy (IT2F) clustering is applied to segments an input image into four clusters. These four clusters from the fuzzy segmentation are further analyzed to extract various stages of retinopathy abnormalities (e.g., Hemorrhage, hard exudates etc.). The extracted blood vessels and optic disc are removed from the analysis to enhance the defects detection process. Experiments were conducted on DIARETDB1 database. The experimental results obtained are validated using the ground-truth images contained in DIARETDB1 database. Impressive results are recorded throughout the experiment. Hard-Exudates and Hemorrhage were detected from the fundus images and results from similarity indexes such as, accuracy (94.11%) sensitivity (93.03%) and specificity (98.45%) were recorded.

Self-Organizing Type-2 Fuzzy Double Loop Recurrent Neural Network for Uncertain Nonlinear System Control.

Nonlinear systems, such as robotic systems, play an increasingly important role in our modern daily life and have become more dominant in many industries; however, robotic control still faces various challenges due to diverse and unstructured work environments. This article proposes a double-loop recurrent neural network (DLRNN) with the support of a Type-2 fuzzy system and a self-organizing mechanism for improved performance in nonlinear dynamic robot control. The proposed network has a double-loop recurrent structure, which enables better dynamic mapping. In addition, the network combines a Type-2 fuzzy system with a double-loop recurrent structure to improve the ability to deal with uncertain environments. To achieve an efficient system response, a self-organizing mechanism is proposed to adaptively adjust the number of layers in a DLRNN. This work integrates the proposed network into a conventional sliding mode control (SMC) system to theoretically and empirically prove its stability. The proposed system is applied to a three-joint robot manipulator, leading to a comparative study that considers several existing control approaches. The experimental results confirm the superiority of the proposed system and its effectiveness and robustness in response to various external system disturbances.

Rainfall Assessment Using Weighted Interval Type-2 Fuzzy Inference System

This study proposes an integrated model for assessing rainfall in Chennai using the Interval type-2 fuzzy reasoning technique and the Analytic Hierarchy Process (AHP). Interval Type-2 Fuzzy Inference System (IT2FIS) is employed to describe complex relationship between the rainfall variables and rainfall rate by considering individual weights of those variables through the aggregation of criteria weights using AHP. The suggested approach has an edge over its predecessors in terms of modeling the inter-personal and intra-personal uncertainty involved in rainfall rate classification. Finally, the developed rainfall model (RM) is applied on the historical dataset collected from Indian Meteorological Department (IMD). The actual data and the IT2FIS model’s performance are compared. Correlation coefficient ([Formula: see text]) and Bland–Altman Plot (BAP) have been used to evaluate the performance metrics of the proposed model. The outcome suggests that the AHP-IT2FIS model can accurately estimate rainfall over time, indicating its potential for long-term use.

Constraint-Driven Type-2 Fuzzy C-Means Clustering and Step-Wise Gossip for Fusion Transmission in Distributed Networks

Constraint-Driven Type-2 Fuzzy C-Means Clustering and Step-Wise Gossip for Fusion Transmission in Distributed Networks