Showing papers on "Fuzzy logic published in 2013"
TL;DR: This paper deals with the adaptive sliding-mode control problem for nonlinear active suspension systems via the Takagi-Sugeno (T-S) fuzzy approach, and a sufficient condition is proposed for the asymptotical stability of the designing sliding motion.
Abstract: This paper deals with the adaptive sliding-mode control problem for nonlinear active suspension systems via the Takagi-Sugeno (T-S) fuzzy approach. The varying sprung and unsprung masses, the unknown actuator nonlinearity, and the suspension performances are taken into account simultaneously, and the corresponding mathematical model is established. The T-S fuzzy system is used to describe the original nonlinear system for the control-design aim via the sector nonlinearity approach. A sufficient condition is proposed for the asymptotical stability of the designing sliding motion. An adaptive sliding-mode controller is designed to guarantee the reachability of the specified switching surface. The condition can be converted to the convex optimization problems. Simulation results for a half-vehicle active suspension model are provided to demonstrate the effectiveness of the proposed control schemes.
653 citations
TL;DR: In this article, an integrated approach of fuzzy multi attribute utility theory and multi-objective programming is presented for rating and selecting the best green suppliers according to economic and environmental criteria and then allocating the optimum order quantities among them.
614 citations
TL;DR: A fuzzy logic expert system is used for battery scheduling and the results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.
Abstract: In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.
561 citations
TL;DR: A novel approach based on TOPSIS and the maximizing deviation method for solving MADM problems, in which the evaluation information provided by the decision maker is expressed in hesitant fuzzy elements and the information about attribute weights is incomplete is developed.
Abstract: Hesitant fuzzy set (HFS), which allows the membership degree of an element to a set represented by several possible values, is considered as a powerful tool to express uncertain information in the process of multi-attribute decision making (MADM) problems. In this paper, we develop a novel approach based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and the maximizing deviation method for solving MADM problems, in which the evaluation information provided by the decision maker is expressed in hesitant fuzzy elements and the information about attribute weights is incomplete. There are two key issues being addressed in this approach. The first one is to establish an optimization model based on the maximizing deviation method, which can be used to determine the attribute weights. According to the idea of the TOPSIS of Hwang and Yoon [1], the second one is to calculate the relative closeness coefficient of each alternative to the hesitant positive-ideal solution, based on which the considered alternatives are ranked and then the most desirable one is selected. An energy policy selection problem is used to illustrate the detailed implementation process of the proposed approach, and demonstrate its validity and applicability. Finally, the extended results in interval-valued hesitant fuzzy situations are also pointed out.
553 citations
TL;DR: An improved fuzzy C-means (FCM) algorithm for image segmentation is presented by introducing a tradeoff weighted fuzzy factor and a kernel metric and results show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.
Abstract: In this paper, we present an improved fuzzy C-means (FCM) algorithm for image segmentation by introducing a tradeoff weighted fuzzy factor and a kernel metric. The tradeoff weighted fuzzy factor depends on the space distance of all neighboring pixels and their gray-level difference simultaneously. By using this factor, the new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhance its robustness to noise and outliers, we introduce a kernel distance measure to its objective function. The new algorithm adaptively determines the kernel parameter by using a fast bandwidth selection rule based on the distance variance of all data points in the collection. Furthermore, the tradeoff weighted fuzzy factor and the kernel distance measure are both parameter free. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.
546 citations
01 Jan 2013
520 citations
Book•
16 Jun 2013
TL;DR: A study of Adaptive Neural Network Control System based on Differential Evolution Algorithm.
Abstract: A Study of Adaptive Neural Network Control System. Zhong, Heng Design of Fuzzy Logic Controller Based on Differential Evolution Algorithm. Shuai, Li (et al.). Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis. Fuzzy Logic and Neural Networks: Basic Concepts and Applications. logic genetic by rajasekaran ebook. srajasekaran and ga vijayalakshmi pai neural networks. MODERN MAGNETIC MATERIALS PRINCIPLES AND APPLICATIONS PDF FREE NETWORKS FUZZY LOGIC AND GENETIC ALGORITHMS SYNTHESIS.
508 citations
TL;DR: Using novel generalizations of the Hukuhara difference for fuzzy sets, new generalized differentiability concepts for fuzzy valued functions are introduced and studied.
497 citations
TL;DR: A new type of fuzzy preference structure, called interval-valued hesitant preference relations, is introduced to describe uncertain evaluation information in group decision making (GDM) processes and is developed in order to consider the differences of opinions between individual decision makers.
Abstract: We introduce a new type of fuzzy preference structure, called interval-valued hesitant preference relations, to describe uncertain evaluation information in group decision making (GDM) processes. Moreover, it allows decision makers to offer all possible interval values that are not accounted for in current preference structure types when one compares two alternatives. We generalize the concept of hesitant fuzzy set (HFS) to that of interval-valued hesitant fuzzy set (IVHFS) in which the membership degrees of an element to a given set are not exactly defined, but denoted by several possible interval values. We give systematic aggregation operators to aggregate interval-valued hesitant fuzzy information. In addition, we develop an approach to GDM based on interval-valued hesitant preference relations in order to consider the differences of opinions between individual decision makers. Numerical examples are provided to illustrate the proposed approach.
466 citations
TL;DR: The interval-valued HFSs and the corresponding correlation coefficient formulas are developed and demonstrated their application in clustering with intervals-valued hesitant fuzzy information through a specific numerical example.
449 citations
TL;DR: Two important clustering algorithms namely centroid based K-means and representative object based FCM (Fuzzy C-Means) clustering algorithm are compared and performance is evaluated on the basis of the efficiency of clustering output.
Abstract: In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled data from the large volume of dataset can be classified initially in an unsupervised fashion by using cluster analysis i.e. clustering the assignment of a set of observations into clusters so that observations in the same cluster may be in some sense be treated as similar. The outcome of the clustering process and efficiency of its domain application are generally determined through algorithms. There are various algorithms which are used to solve this problem. In this research work two important clustering algorithms namely centroid based K-Means and representative object based FCM (Fuzzy C-Means) clustering algorithms are compared. These algorithms are applied and performance is evaluated on the basis of the efficiency of clustering output. The numbers of data points as well as the number of clusters are the factors upon which the behaviour patterns of both the algorithms are analyzed. FCM produces close results to K-Means clustering but it still requires more computation time than K-Means clustering. Keywords—clustering; k-means; fuzzy c-means; time complexity
TL;DR: Several series of aggregation operators are proposed and two methods are proposed to determine the aggregation weight vectors based on the support degrees among aggregation arguments, so that the weight vector of decision makers are obtained more objectively.
Abstract: Hesitancy is the most common problem in decision making, for which hesitant fuzzy set can be considered as a suitable means allowing several possible degrees for an element to a set. In this paper, we study the aggregation of the hesitancy fuzzy information. Several series of aggregation operators are proposed and the connections of them are discussed. To reflect the correlation of the aggregation arguments, two methods are proposed to determine the aggregation weight vectors. Based on the support degrees among aggregation arguments, the weight vector of decision makers are obtained more objectively. To deal with the correlation of criteria, we apply the Choquet integral to get the weights of criteria. A method is also proposed for group decision making under hesitant fuzzy environment.
TL;DR: The proposed adaptive fuzzy tracking controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value.
Abstract: This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.
TL;DR: The primary goal of the study is to suggest the systematic transformation of the entropy into the similarity measure for HFSs and vice versa, and two clustering algorithms are developed under a hesitant fuzzy environment.
TL;DR: A new adaptive fuzzy output feedback control approach is developed via the backstepping recursive design technique and it is shown that the proposed control approach can assure that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded.
Abstract: This paper is concerned with the problem of adaptive fuzzy tracking control for a class of multi-input and multi-output (MIMO) strict-feedback nonlinear systems with both unknown nonsymmetric dead-zone inputs and immeasurable states. In this research, fuzzy logic systems are utilized to evaluate the unknown nonlinear functions, and a fuzzy adaptive state observer is established to estimate the unmeasured states. Based on the information of the bounds of the dead-zone slopes as well as treating the time-varying inputs coefficients as a system uncertainty, a new adaptive fuzzy output feedback control approach is developed via the backstepping recursive design technique. It is shown that the proposed control approach can assure that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded. It is also shown that the observer and tracking errors converge to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.
Book•
28 May 2013
TL;DR: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence.
Abstract: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples.Key features:Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB exercises and applications in each chapterPresents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systemsConsiders real world problems in the domain of systems modelling, control and optimizationContains a foreword written by Lotfi ZadehComputational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.
TL;DR: This paper develops a wide range of hesitant fuzzy power aggregation operators for hesitant fuzzy information and demonstrates several useful properties of the operators and discusses the relationships between them.
TL;DR: The problems of stability and tracking control for a class of large-scale nonlinear systems with unmodeled dynamics are addressed by designing the decentralized adaptive fuzzy output feedback approach using the Lyapunov stability method.
Abstract: In this paper, the problems of stability and tracking control for a class of large-scale nonlinear systems with unmodeled dynamics are addressed by designing the decentralized adaptive fuzzy output feedback approach. Because the dynamic surface control technique is introduced, the designed controllers can avoid the issue of “explosion of complexity,” which comes from the traditional backstepping design procedure that deals with large-scale nonlinear systems with unmodeled dynamics. In addition, a reduced-order observer is designed to estimate those immeasurable states. Based on the Lyapunov stability method, it is proven that all the signals in the closed-loop system are bounded, and the system outputs track the reference signals to a small neighborhood of the origin by choosing the design parameters appropriately. The simulation examples are given to verify the effectiveness of the proposed techniques.
01 Apr 2013
TL;DR: A fuzzy energy-aware unequal clustering algorithm (EAUCF), that addresses the hot spots problem, and is compared with some popular clustering algorithms in the literature, namely Low Energy Adaptive Clustering Hierarchy, Cluster-Head Election Mechanism using Fuzzy Logic and Energy-Efficient Unequal Clustered.
Abstract: In order to gather information more efficiently in terms of energy consumption, wireless sensor networks (WSNs) are partitioned into clusters. In clustered WSNs, each sensor node sends its collected data to the head of the cluster that it belongs to. The cluster-heads are responsible for aggregating the collected data and forwarding it to the base station through other cluster-heads in the network. This leads to a situation known as the hot spots problem where cluster-heads that are closer to the base station tend to die earlier because of the heavy traffic they relay. In order to solve this problem, unequal clustering algorithms generate clusters of different sizes. In WSNs that are clustered with unequal clustering, the clusters close to the base station have smaller sizes than clusters far from the base station. In this paper, a fuzzy energy-aware unequal clustering algorithm (EAUCF), that addresses the hot spots problem, is introduced. EAUCF aims to decrease the intra-cluster work of the cluster-heads that are either close to the base station or have low remaining battery power. A fuzzy logic approach is adopted in order to handle uncertainties in cluster-head radius estimation. The proposed algorithm is compared with some popular clustering algorithms in the literature, namely Low Energy Adaptive Clustering Hierarchy, Cluster-Head Election Mechanism using Fuzzy Logic and Energy-Efficient Unequal Clustering. The experiment results show that EAUCF performs better than the other algorithms in terms of first node dies, half of the nodes alive and energy-efficiency metrics in all scenarios. Therefore, EAUCF is a stable and energy-efficient clustering algorithm to be utilized in any WSN application.
01 Jan 2013
TL;DR: An attribute selection method based on fuzzy gain ratio under the framework of fuzzy rough set theory is proposed and is compared to several other approaches on three real world tumor data sets in gene expression to show that the proposed method is effective.
Abstract: Tumor classification based on gene expression levels is important for tumor diagnosis. Since tumor data in gene expression contain thousands of attributes, attribute selection for tumor data in gene expression becomes a key point for tumor classification. Inspired by the concept of gain ratio in decision tree theory, an attribute selection method based on fuzzy gain ratio under the framework of fuzzy rough set theory is proposed. The approach is compared to several other approaches on three real world tumor data sets in gene expression. Results show that the proposed method is effective. This work may supply an optional strategy for dealing with tumor data in gene expression or other applications.
TL;DR: The classical VIKOR method is extended to accommodate hesitant fuzzy circumstances, and a practical example is provided to show that the method is very effective in solving multi-criteria decision making problems with hesitant preference information.
Abstract: Since it was firstly introduced by Torra and Narukawa (The 18th IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 2009, pp. 1378---1382), the hesitant fuzzy set has attracted more and more attention due to its powerfulness and efficiency in representing uncertainty and vagueness. This paper extends the classical VIKOR (vlsekriterijumska optimizacija i kompromisno resenje in serbian) method to accommodate hesitant fuzzy circumstances. Motivated by the hesitant normalized Manhattan distance, we develop the hesitant normalized Manhattan $$L_p$$ --metric, the hesitant fuzzy group utility measure, the hesitant fuzzy individual regret measure, and the hesitant fuzzy compromise measure. Based on these new measures, we propose a hesitant fuzzy VIKOR method, and a practical example is provided to show that our method is very effective in solving multi-criteria decision making problems with hesitant preference information.
TL;DR: This paper investigates the problem of robust H∞ output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints and designs a suitable constrained piecewise affine static output feedback controller.
Abstract: This paper investigates the problem of robust H∞ output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. The objective is to design a suitable constrained piecewise affine static output feedback controller, guaranteeing the asymptotic stability of the resulting closed-loop fuzzy control system with a prescribed H∞ disturbance attenuation level. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, three examples are provided to illustrate the effectiveness of the proposed methods.
01 Feb 2013
TL;DR: Based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented, proving the effectiveness, robustness and compatibility of the ICA-ANN model.
Abstract: Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.
TL;DR: A fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm yields sufficient and sensible imputation performance results.
TL;DR: An integrated approach, with a fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) as its important elements, has been used for capturing the subjectivity of the situation with a final conversion to a crisp value which is much more comprehensible.
Abstract: Risk is inherent in almost every activity of supply chain management. With the ever-increasing push for efficiency, supply chains today are getting more and more risky. Adding to the difficulty of dealing with these risks is the amount of subjectivity and uncertainty involved. This makes analytical examination of the situation very difficult, especially as the amount of information available at a particular time is not sufficient for such an analysis. Thus a supply chain risk index, which captures the level of risk faced by a supply chain in a given situation, is the need of the hour. This study is an effort towards quantifying the risks in a supply chain and then consolidating the values into a comprehensive risk index. An integrated approach, with a fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) as its important elements, has been used for this purpose. Fuzzy values in this study help in capturing the subjectivity of the s...
TL;DR: Simulation results show that the proposed approach to the convergence and diversity of the swarm in PSO using fuzzy logic improves the performance of PSO.
Abstract: In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.
TL;DR: A novel fuzzy Likert scale developed based on the fuzzy sets theory can capture the lost information and regulate the distorted information arising from the closed-form scaling and the ordinal nature of this measurement method.
Abstract: The Likert method is commonly used as a standard psychometric scale to measure responses. This measurement scale has a procedure that facilitates survey construction and administration, and data coding and analysis. However, there are some drawbacks in the Likert scaling. This paper addresses the information distortion and information lost arising from the closed-form scaling and the ordinal nature of this measurement method. To overcome these problems, a novel fuzzy Likert scale developed based on the fuzzy sets theory has been proposed. The major contribution of the fuzzy Likert approach is that it permits partial agreement of a scale point. By incorporating this capability into the measurement process, the new scale can capture the lost information and regulate the distorted information. A quantitative analysis based on the concept Consensus has proven that the new scale can provide a more accurate measurement. The implementation feasibility and the improved measurement performance of the fuzzy Likert scale have been demonstrated via a simulation study on a low birth weight analysis.
TL;DR: The results show that the development of fuzzy-appearance manifold and fuzzy-nearest distance calculation in the eigenspace domain for pose estimation of degraded face images could maintain high recognition rates for estimating the pose position of the degradation face images.
Abstract: This paper presents the development of fuzzy- appearance manifold and fuzzy-nearest distance calculation in the eigenspace domain for pose estimation of degraded face images. In order to obtain a robust pose estimation system which can deal with the fuzziness of face data caused by statistical errors, we proposed the fuzzy-vector representation in eigenspace domain of the face images. Using fuzzy-vector representations, all of the crisp vectors of face data in the eigenspace domain are firstly transformed into fuzzy-vectors as fuzzy-points. Next, the fuzzy-appearance manifold is constructed from all the available fuzzy-points and the fuzzy-nearest distance calculation is proposed as the classifier of the pose estimation system. The pose estimation of an unknown face image is performed by firstly being projected onto the eigenspace domain then transformed to become an unknown fuzzy-point, and its fuzzy- distance with all of the available fuzzy-points in the fuzzy-appearance manifold will be calculated. The fuzzy- point in the manifold which has the nearest distance to that unknown fuzzy-point will be determined as the pose position of the unknown face image. In the experiment, face images with various quality degradation effects were used. The results show that the system could maintain high recognition rates for estimating the pose position of the degraded face images.
TL;DR: The HIVFCOA and HIVFCOG operators are applied to multiple attribute decision making with hesitant interval-valued fuzzy information and an illustrative example has been given to show the developed method.
Abstract: In this paper, we investigate the multiple attribute decision making (MADM) problems in which attribute values take the form of hesitant interval-valued fuzzy information. Firstly, definition and some operational laws of hesitant interval-valued fuzzy elements and score function of hesitant interval-valued fuzzy elements are introduced. Then, we have developed some hesitant interval-valued fuzzy aggregation operators: hesitant interval-valued fuzzy weighted averaging (HIVFWA) operator, hesitant interval-valued fuzzy ordered weighted averaging (HIVFOWA) operator, the hesitant interval-valued fuzzy weighted geometric (HIVFWG) operator, hesitant interval-valued fuzzy ordered weighted geometric (HIVFOWG) operator, hesitant interval-valued fuzzy choquet ordered averaging (HIVFCOA) operator, hesitant interval-valued fuzzy choquet ordered geometric (HIVFCOG) operator, hesitant interval-valued fuzzy prioritized aggregation operators and hesitant interval-valued fuzzy power aggregation operators. We have applied the HIVFCOA and HIVFCOG operators to multiple attribute decision making with hesitant interval-valued fuzzy information. Finally an illustrative example has been given to show the developed method.
TL;DR: An overview and comparison of three categories of methods to reduce their computational cost will help researchers and practitioners on IT2 FLSs choose the most suitable structure and type-reduction algorithms, from a computational cost perspective.
Abstract: Interval type-2 fuzzy logic systems (IT2 FLSs) have demonstrated better abilities to handle uncertainties than their type-1 (T1) counterparts in many applications; however, the high computational cost of the iterative Karnik-Mendel (KM) algorithms in type-reduction means that it is more expensive to deploy IT2 FLSs, which may hinder them from certain cost-sensitive real-world applications. This paper provides a comprehensive overview and comparison of three categories of methods to reduce their computational cost. The first category consists of five enhancements to the KM algorithms, which are the most popular type-reduction algorithms to date. The second category consists of 11 alternative type-reducers, which have closed-form representations and, hence, are more convenient for analysis. The third category consists of a simplified structure for IT2 FLSs, which can be combined with any algorithms in the first or second category for further computational cost reduction. Experiments demonstrate that almost all methods in these three categories are faster than the KM algorithms. This overview and comparison will help researchers and practitioners on IT2 FLSs choose the most suitable structure and type-reduction algorithms, from a computational cost perspective. A recommendation is given in the conclusion.