Type-2 Fuzzy Research Articles

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

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey

The determination of the areas where the solar power plant will be installed is of great importance for the performance of the solar power plant. Solar and hydroelectric energy are the most widely used renewable energy sources in Kars province. Site selection for these power plants is an important factor in terms of reducing the installation cost of the solar power plant and achieving maximum efficiency during operation. Determining the areas where the power plants will be installed is a very complex and difficult to analyse spatial decision making problem. In this study, firstly GIS is used as a mapping method to obtain the locations of both solar power plants in Susuz, Arpaçay, Akkaya, Kars city centre, Selim, Digor, Kağızman and Sarıkamıș districts of Kars province and then Taguchi loss function based interval type-2 fuzzy approach is applied to the problem. In order to obtain more accurate results, the results of the two methods (GIS and Taguchi loss function based interval type-2 fuzzy approach) were also compared. According to the solar power plant map obtained, it was determined that the total area of suitable areas is 78600 km2.

Analytical Expressions for Similarity Measure Between Gaussian Interval Type-2 Fuzzy Sets With Uncertain Means

Analytical Expressions for Similarity Measure Between Gaussian Interval Type-2 Fuzzy Sets With Uncertain Means

Intelligent Control System based on Wavelet Type-2 Fuzzy Neural network Design For Robot System

Intelligent Control System based on Wavelet Type-2 Fuzzy Neural network Design For Robot System

Attribute Reduction of Type-2 Fuzzy Information Systems Based on $$\sigma $$ Self-information

Attribute Reduction of Type-2 Fuzzy Information Systems Based on $$\sigma $$ Self-information

Optimized fuzzy type 1 Mamdani controller to enhance transient stability of AC-power system

Optimized fuzzy type 1 Mamdani controller to enhance transient stability of AC-power system

A novel four‐gene‐based inheritance model explains gradations of nakedness in cotton (Gossypium hirsutum L.)

AbstractIn crop improvement, delineating inheritance pattern of a character guides a plant breeder to choose appropriate breeding procedure, which enables ease of improving phenotypic expression of the traits. To deduce the inheritance pattern of naked seed or fuzzless character, four parental lines involving two purely naked seed types (DHN‐1 and DHN‐10), one partial naked seed type (SP46CC) and one fully fuzzy type (OSB415) were used to develop three different types of crosses, namely, naked × naked seed type cross (N × N), partial naked × naked seed type cross (PN × N) and fuzzy × naked seed type cross (F × N). We observed that naked trait segregates in the ratio of 11 naked: 5 partial naked in N × N cross, 22 naked: 42 partial naked in PN × N cross and 22 naked: 42 partial naked: 192 fuzzy in F × N cross. Our study reports a novel four‐gene model to understand inheritance nature of naked seed character where the presence of single basic dominant fuzzy gene negates the effect of genes controlling nakedness resulting fuzzy phenotype irrespective of the number of partial fuzzyness/nakedness imparting alleles. This indicates that nakedness is recessive to fuzzyness. Based on our results, we infer observing gradations of nakedness on seeds depends on the presence of number of partial fuzzyness imparting alleles, which led to a conclusion that the trait exhibited dose‐dependent expression with one basic gene for fuzziness.

A Low Power Analog Integrated Fractional Order Type-2 Fuzzy PID Controller

A Low Power Analog Integrated Fractional Order Type-2 Fuzzy PID Controller

A novel interval type-2 fuzzy consensus reaching process model and group decision-making method for renewable energy investment

Renewable Energy Investment (REI) is a critical challenge for both governments and investors, often complicated by conflicting criteria and uncertainties that must be managed to obtain reliable and useful decisions. To address the REI problems, a novel group decision-making approach under uncertainty is proposed. Our approach introduces the Interval Type-2 Fuzzy Sets (IT2FS) to construct linguistic terms that reflect their uncertainty and personalized semantics. Subsequently, we develop a novel automatic interval type-2 fuzzy Consensus Reaching Process (CRP) model to detect disagreements among experts and generate personalized recommendations to improve the consensus level. The proposed CRP model conducts interval analysis to facilitate consensus adjustments, thereby enhancing its capacity to describe uncertainty. Once the CRP is finished, the prospect theory is extended to the IT2FS to calculate the overall values for each alternative, which can capture the decision makers’ attitudes towards risk and uncertainty. Given that prospect values are characterized by interval values, we propose a two-stage interval-valued ranking approach to compare and rank them. Finally, an illustrative case study is provided to demonstrate the feasibility of our approach, along with comparative analysis to highlight its advantages and sensitivity analysis to show the robustness of parameters on decision results.