Showing papers on "Fuzzy logic published in 2017"
TL;DR: The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.
Abstract: Fuzzy best-worst method is proposed to solve the issues under fuzzy environment.A consistency ratio for fuzzy best-worst method is proposed for verification.The results indicate the fuzzy best-worst method outperforms best-worst method.The fuzzy best-worst method has a higher comparison consistency. Considering the vagueness frequently representing in decision data due to the lack of complete information and the ambiguity arising from the qualitative judgment of decision-makers, the crisp values of criteria may be inadequate to model the real-life multi-criteria decision-making (MCDM) issues. In this paper, the latest MCDM method, namely best-worst method (BWM) was extended to the fuzzy environment. The reference comparisons for the best criterion and for the worst criterion were described by linguistic terms of decision-makers, which can be expressed in triangular fuzzy numbers. Then, the graded mean integration representation (GMIR) method was employed to calculate the weights of criteria and alternatives with respect to different criteria under fuzzy environment. According to the concept of BWM, the nonlinearly constrained optimization problem was built for determining the fuzzy weights of criteria and alternatives with respect to different criteria. The fuzzy ranking scores of alternatives can be derived from the fuzzy weights of alternatives with respect to different criteria multiplied by fuzzy weights of the corresponding criteria, and then the crisp ranking score of alternatives can be calculated by employing GMIR method for optimal alternative selection. Meanwhile, the consistency ratio was proposed for fuzzy BWM to check the reliability of fuzzy preference comparisons. Three case studies were performed to illustrate the effectiveness and feasibility of the proposed fuzzy BWM. The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.
534 citations
TL;DR: The TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) technique is extended to solve MCGDM problems within the context of interval type-2 fuzzy sets (IT2FSs) and presented its application to green supplier selection problem.
479 citations
TL;DR: A new robust adaptive fuzzy backstepping stabilization control strategy is developed based on the common Lyapunov stability theory and stochastic small-gain theorem and the stability of the closed-loop system on input-state-practically stable in probability is proved.
Abstract: This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.
381 citations
TL;DR: Results show that the present method and simulated annealing provide a good scheduling methodology to solve fuzzy Fredholm–Volterra integrodifferential equations.
Abstract: In this article, we propose the reproducing kernel Hilbert space method to obtain the exact and the numerical solutions of fuzzy Fredholm---Volterra integrodifferential equations. The solution meth...
332 citations
TL;DR: The prediction performances of EFNN are better than those of traditional models due to their strong learning ability and as the prediction time step increases, the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted errors and slow raising rate of errors.
Abstract: This paper proposes a new method in construction fuzzy neural network to forecast travel speed for multi-step ahead based on 2-min travel speed data collected from three remote traffic microwave sensors located on a southbound segment of a fourth ring road in Beijing City. The first-order Takagi–Sugeno system is used to complete the fuzzy inference. To train the evolving fuzzy neural network (EFNN), two learning processes are proposed. First, a $K$ -means method is employed to partition input samples into different clusters and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated. Second, a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi–Sugeno type fuzzy rules. Furthermore, a trigonometric regression function is introduced to capture the periodic component in the raw speed data. Specifically, the predicted performance between the proposed model and six traditional models are compared, which are artificial neural network, support vector machine, autoregressive integrated moving average model, and vector autoregressive model. The results suggest that the prediction performances of EFNN are better than those of traditional models due to their strong learning ability. As the prediction time step increases, the EFNN model can consider the periodic pattern and demonstrate advantages over other models with smaller predicted errors and slow raising rate of errors.
316 citations
TL;DR: It is shown that by invoking the redundancy properties induced by the descriptor formulation, combined with some convexifying techniques, the existence of the desired reliable controller can be explicitly determined by the solution of a convex optimization problem.
Abstract: This article studies the robust and reliable $\mathscr {H}_{\infty }$ static output feedback (SOF) control for nonlinear systems with actuator faults in a descriptor system framework. The nonlinear plant is characterized by a discrete-time Takagi-Sugeno (T-S) fuzzy affine model with parameter uncertainties, and the Markov chain is utilized to describe the actuator-fault behaviors. Specifically, by adopting a state-output augmentation approach, the original system is firstly reformulated into the descriptor fuzzy affine system. Based upon a novel piecewise Markovian Lyapunov function (LF), the $\mathscr {H}_{\infty }$ performance analysis condition for the underlying system is then presented, and furthermore the robust and reliable SOF controller synthesis is carried out. It is shown that by invoking the redundancy properties induced by the descriptor formulation, combined with some convexifying techniques, the existence of the desired reliable controller can be explicitly determined by the solution of a convex optimization problem. Finally, simulation studies are applied to confirm the effectiveness of the developed method.
316 citations
TL;DR: It is proven that all the signals in the closed-loop switched system are bounded, and the system output converges to a small neighborhood of the origin.
Abstract: This paper proposes an fuzzy adaptive output-feedback stabilization control method for nonstrict feedback uncertain switched nonlinear systems. The controlled system contains unmeasured states and unknown nonlinearities. First, a switched state observer is constructed in order to estimate the unmeasured states. Second, a variable separation approach is introduced to solve the problem of nonstrict feedback. Third, fuzzy logic systems are utilized to identify the unknown uncertainties, and an adaptive fuzzy output feedback stabilization controller is set up by exploiting the backstepping design principle. At last, by applying the average dwell time method and Lyapunov stability theory, it is proven that all the signals in the closed-loop switched system are bounded, and the system output converges to a small neighborhood of the origin. Two examples are given to further show the effectiveness of the proposed switched control approach.
311 citations
TL;DR: A practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness in solving the multiple attribute decision making problems with picture fuzzy information.
Abstract: In this paper, we investigate the multiple attribute decision making problems with picture fuzzy information. Then, we utilize arithmetic and geometric operations to develop some picture fuzzy aggregation operators: picture fuzzy weighted average (PFWA) operator, picture fuzzy weighted geometric (PFWG) operator, picture fuzzy ordered weighted average (PFOWA) operator, picture fuzzy ordered weighted geometric (PFOWG) operator, picture fuzzy hybrid average (PFHA) operator and picture fuzzy hybrid geometric (PFHG) operator. The prominent characteristic of these proposed operators are studied. Then, we have utilized these operators to develop some approaches to solve the picture fuzzy multiple attribute decision making problems. Finally, a practical example for enterprise resource planning (ERP) system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.
310 citations
TL;DR: The objective of this paper is to present some series of geometric‐aggregated operators under Pythagorean fuzzy environment by relaxing the condition that the sum of the degree of membership functions is less than one with the square sum ofthe degree of membership functions isLess than one.
Abstract: The objective of this paper is to present some series of geometric-aggregated operators under Pythagorean fuzzy environment by relaxing the condition that the sum of the degree of membership functions is less than one with the square sum of the degree of membership functions is less than one. Under these environments, aggregator operators, namely, Pythagorean fuzzy Einstein weighted geometric, Pythagorean fuzzy Einstein ordered weighted geometric, generalized Pythagorean fuzzy Einstein weighted geometric, and generalized Pythagorean fuzzy Einstein ordered weighted geometric operators, are proposed in this paper. Some of its properties have also been investigated in details. Finally, an illustrative example for multicriteria decision-making problems of alternatives is taken to demonstrate the effectiveness of the approach.
304 citations
TL;DR: In order to overcome the difficulty of controller design for nonstrict-feedback system in backstepping design process, a variables separation method is introduced and an adaptive fuzzy controller is designed to guarantee all the signals of the resulting closed-loop system to be bounded.
Abstract: This paper investigates the problem of adaptive fuzzy state-feedback control for a category of single-input and single-output nonlinear systems in nonstrict-feedback form. Unmodeled dynamics and input constraint are considered in the system. Fuzzy logic systems are employed to identify unknown nonlinear characteristics existing in systems. An appropriate Lyapunov function is chosen to ensure unmodeled dynamics to be input-to-state practically stable. A smooth function is introduced to tackle input saturation. In order to overcome the difficulty of controller design for nonstrict-feedback system in backstepping design process, a variables separation method is introduced. Moreover, based on small-gain technique, an adaptive fuzzy controller is designed to guarantee all the signals of the resulting closed-loop system to be bounded. Finally, two illustrative examples are given to validate the effectiveness of the new design techniques.
283 citations
TL;DR: It is demonstrated that the proposed consensus scheme can steer a multiagent system synchronizing to the predefined reference signals, based on Lyapunov stability theory, and can greatly alleviate the computation burden.
Abstract: Compared with the existing neural network (NN) or fuzzy logic system (FLS) based adaptive consensus methods, the proposed approach can greatly alleviate the computation burden because it needs only to update a few adaptive parameters online. In the multiagent agreement control, the system uncertainties derive from the unknown nonlinear dynamics are counteracted by employing the adaptive NNs; the state delays are compensated by designing a Lyapunov–Krasovskii functional. Finally, based on Lyapunov stability theory, it is demonstrated that the proposed consensus scheme can steer a multiagent system synchronizing to the predefined reference signals. Two simulation examples, a numerical multiagent system and a practical multimanipulator system, are carried out to further verify and testify the effectiveness of the proposed agreement approach.
01 Dec 2017
TL;DR: This paper investigates the analytic and approximate solutions of second-order, two-point fuzzy boundary value problems based on the reproducing kernel theory under the assumption of strongly generalized differentiability.
Abstract: In this paper, we investigate the analytic and approximate solutions of second-order, two-point fuzzy boundary value problems based on the reproducing kernel theory under the assumption of strongly generalized differentiability. The solution methodology is based on generating the orthogonal basis from the obtained kernel functions, while the orthonormal basis is constructing in order to formulate and utilize the solutions with series form in terms of their r-cut representation in the space $$\oplus _{j=1}^2 W_2^3 \left[ {a,b}\right] $$źj=12W23a,b. An efficient computational algorithm is provided to guarantee the procedure and to confirm the performance of the proposed method. Results of numerical experiments are provided to illustrate the theoretical statements in order to show potentiality, generality, and superiority of our algorithm for solving such fuzzy equations. Graphical results, tabulated data, and numerical comparisons are presented and discussed quantitatively to illustrate the possible fuzzy solutions.
TL;DR: An adaptive fuzzy backstepping control method for a class of uncertain fractional-order nonlinear systems with unknown external disturbances that ensures convergence of the tracking error is constructed.
Abstract: Backstepping control is effective for integer-order nonlinear systems with triangular structures. Nevertheless, it is hard to be applied to fractional-order nonlinear systems as the fractional-order derivative of a compound function is very complicated. In this paper, we develop an adaptive fuzzy backstepping control method for a class of uncertain fractional-order nonlinear systems with unknown external disturbances. In each step, a complicated unknown nonlinear function produced by differentiating a compound function with a fractional order is approximated by a fuzzy logic system, and a virtual control law is designed based on the fractional Lyapunov stability criterion. At the last step, an adaptive fuzzy controller that ensures convergence of the tracking error is constructed. The effectiveness of the proposed method has been verified by two simulation examples.
TL;DR: The primary goal of the study is to suggest the systematic transformation of information measures (distance measure, similarity measure, entropy, inclusion measure) for PFSs and to show the efficiency of the proposed similarity measure.
Abstract: Pythagorean fuzzy set (PFS), originally proposed by Yager, is more capable than intuitionistic fuzzy set (IFS) to handle vagueness in the real world. The main purpose of this paper is to investigate the relationship between the distance measure, the similarity measure, the entropy, and the inclusion measure for PFSs. The primary goal of the study is to suggest the systematic transformation of information measures (distance measure, similarity measure, entropy, inclusion measure) for PFSs. For achieving this goal, some new formulae for information measures of PFSs are introduced. To show the efficiency of the proposed similarity measure, we apply it to pattern recognition, clustering analysis, and medical diagnosis. Some illustrative examples are given to support the findings and also demonstrate their practicality and effectiveness of similarity measure between PFSs.
TL;DR: It is shown how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation and the fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.
Abstract: Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-driven feature learning from big data. However, typical DL is a fully deterministic model that sheds no light on data uncertainty reductions. In this paper, we show how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation. The bulk of the proposed fuzzy system is a hierarchical deep neural network that derives information from both fuzzy and neural representations. Then, the knowledge learnt from these two respective views are fused altogether forming the final data representation to be classified. The effectiveness of the model is verified on three practical tasks of image categorization, high-frequency financial data prediction and brain MRI segmentation that all contain high level of uncertainties in the raw data. The fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.
TL;DR: A novel adaptive fuzzy tracking control scheme is developed to guarantee all variables of the closed-loop systems are semiglobally uniformly ultimately bounded, and the tracking error can be adjusted around the origin with a small neighborhood.
Abstract: This paper investigates the problem of adaptive fuzzy tracking control for nonlinear strict-feedback systems with input delay and output constraint. Input delay is handled based on the information of Pade approximation and output constraint problem is solved by barrier Lypaunov function. Some adaptive parameters of the controller need to be updated online through considering the norm of membership function vector instead of all sub-vectors. A novel adaptive fuzzy tracking control scheme is developed to guarantee all variables of the closed-loop systems are semiglobally uniformly ultimately bounded, and the tracking error can be adjusted around the origin with a small neighborhood. The stability of the closed-loop systems is proved and simulation results are given to demonstrate the effectiveness of the proposed control approach.
TL;DR: This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi–Sugeno (T–S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions.
Abstract: This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi–Sugeno (T–S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T–S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T–S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori . Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.
TL;DR: Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances.
TL;DR: The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.
Abstract: This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.
TL;DR: A hybrid model is proposed to identify the most sustainable supplier with respect to the determined attributes using an Iranian textile manufacturing company as case study and the results show that economic aspect is still the most essential aspect, followed by environmental aspect and finally social aspect.
TL;DR: A new fuzzy dynamic output-feedback fuzzy controller that takes into consideration the abstract energy, storage function, and supply rate for the disturbance attenuation and provides a unified framework that can incorporate existing results for T–S fuzzy systems with time-varying input delay and output constraints is developed.
Abstract: This paper develops a new fuzzy dynamic output-feedback control scheme for Takagi–Sugeno (T–S) fuzzy systems with time-varying input delay and output constraints based on $(Q,S,R)$ - $\alpha$ -dissipativity. The proposed controller, called a $(Q,S,R)$ - $\alpha$ -dissipative output-feedback fuzzy controller, takes into consideration the abstract energy, storage function, and supply rate for the disturbance attenuation and provides a unified framework that can incorporate existing results for $\mathcal {H}_{\infty }$ and passivity controllers as special cases for T–S fuzzy systems with time-varying input delay and output constraints. A dynamic parallel distributed compensator is used to design the $(Q,S,R)$ - $\alpha$ -dissipative output-feedback fuzzy controller to ensure the asymptotic stability and strict $(Q,S,R)$ - $\alpha$ -dissipativity of closed-loop systems described by a T–S fuzzy model that satisfies some output constraints. By employing the reciprocally convex approach, a new set of delay-dependent conditions for the desired controller is formulated in terms of the linear matrix inequality. The effectiveness and the applicability of the proposed design techniques are validated by an example of control for active suspension systems for different road conditions.
TL;DR: A novel approach is introduced to tackle unknown functions with nonstrict-feedback structure in the design process, and by introducing an auxiliary system, the input saturation problem can be solved and a novel adaptive fuzzy tracking controller is designed.
Abstract: This paper studies an adaptive fuzzy tracking control problem for nonlinear stochastic systems with input saturation and nonstrict-feedback form. We use fuzzy logic systems to approximate unknown nonlinear functions. A novel approach is introduced to tackle unknown functions with nonstrict-feedback structure in the design process. By introducing an auxiliary system, the input saturation problem can be solved. Moreover, based on backstepping control design approach, a novel adaptive fuzzy tracking controller is designed to guarantee all signals in the closed-loop system to be bounded, and the system output can be driven to track the trajectory of a given reference signal. Finally, some simulation results are given to confirm the effectiveness of the proposed approach.
TL;DR: A MCDM methodology based on type-2 fuzzy sets whose membership functions are also fuzzy and hesitant fuzzy sets that enable to handle situations that an element has several membership value are more able to model uncertainties in decision making process are suggested to evaluate renewable energy alternatives for Turkey.
Abstract: Nowadays, energy demand is increasing as a result of growing population all over the world. Current conventional sources are not an adequate level in order to meet this energy requirement. Therefore, it is necessary to consider economic and clean alternative energy sources. In this context, renewable energy sources can be contemplated as a solution for this energy problem. On the other hand, selection among energy alternatives is a multi-criteria decision making (MCDM) problem and it is necessary to make an assessment in terms of several conflicting criteria. Sometimes, it may not be easy to evaluate these criteria by using crisp numbers and we need to evaluate by using human judgements and linguistic terms that can be used for a more flexible and sensitive evaluation. However, the fuzzy sets enable to cope with vagueness of evaluations in decision making process. In this study, an integrated MCDM model based on the fuzzy sets is proposed for prioritization of renewable energy alternatives in Turkey. The suggested fuzzy MCDM model combines analytic hierarchy process (AHP) based on interval type-2 fuzzy sets and hesitant fuzzy TOPSIS methods. Since the type-2 fuzzy sets whose membership functions are also fuzzy and hesitant fuzzy sets that enable to handle situations that an element has several membership value are more able to model uncertainties in decision making process, in this paper a MCDM methodology based on these two methods are suggested to evaluate renewable energy alternatives for Turkey. Interval type-2 fuzzy AHP method is applied to determine the weights of decision criteria, and hesitant fuzzy TOPSIS method is applied to prioritize renewable energy alternatives. A real case application has been presented via expert evaluations to indicate applicability of the proposed model. Besides, a sensitivity analysis has been performed to examine the effects of main criteria weights in ranking.
TL;DR: It is proved that the overall closed-loop system is stable in the sense of semi-globally uniformly ultimately bounded in mean square, and the output of the switched system converges to a small neighborhood of the origin with appropriate choice of design parameters.
Abstract: This paper considers the problem of adaptive fuzzy backstepping-based output-feedback controller design for a class of uncertain switched nonlinear stochastic systems in lower-triangular form without the measurements of the system states. By combining fuzzy logic systems’ universal approximation ability and dynamic surface control technique in the adaptive backstepping recursive design with a modified average dwell-time scheme, a new adaptive fuzzy control approach is presented for the switched system. More specifically, a switched observer is constructed to reduce the conservativeness aroused by the employ of a common observer, and individual coordinate transformations for subsystems are given up by adopting a common coordinate transformation of all subsystems. It is proved that the overall closed-loop system is stable in the sense of semi-globally uniformly ultimately bounded in mean square, and the output of the switched system converges to a small neighborhood of the origin with appropriate choice of design parameters. Finally, simulation studies are provided to demonstrate the validity of the proposed control method.
01 Aug 2017
TL;DR: In this article, a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including step-wise weight assessment ratio analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) is presented.
Abstract: The Multiple Criteria Decision Making (MCDM) utility determining approaches and fuzzy sets are considered to be new development approaches, which have been recently presented, extended, and used by some scholars in area of decision making. There is a lack of research regarding to systematic literature review and classification of study about these approaches. Therefore; in the present study, the attempt is made to present a systematic review of methodologies and applications with recent fuzzy developments of two new MCDM utility determining approaches including Step-wise Weight Assessment Ratio Analysis (SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) and fuzzy extensions which discussed in recent years. Regarding this, some major databases including Web of Science, Scopus and Google Scholar have been nominated and systematic and meta-analysis method which called “PRISMA” has been proposed. In addition, the selected articles were classified based on authors, the year of publication, journals and conferences names, the technique and method used, research objectives, research gap and problem, solution and modeling, and finally results and findings. The results of this study can assist decision-makers in handling information such as stakeholders’ preferences, interconnected or contradictory criteria and uncertain environments. In addition, findings of this study help to practitioners and academic for adopting the new MCDM utility techniques such as WASPAS and SWARA in different application areas and presenting insight into literature.
TL;DR: This paper proposes a hesitant fuzzy number with probabilities, called the hesitant probabilistic fuzzy number, and construct its score function, deviation function, comparison laws, and its basic operations and a practical case is provided to demonstrate consensus building with a group of DMs under the HPFE environment.
Abstract: As a generalized fuzzy number, the hesitant fuzzy element (HFE) has been receiving increased attention and has recently become a popular topic. However, we find that the occurring probabilities of the possible values in the HFE are equal, which is obviously impractical. Consequently, in this paper, we propose a hesitant fuzzy number with probabilities, called the hesitant probabilistic fuzzy number, and construct its score function, deviation function, comparison laws, and its basic operations. It is well known that in the context of a group of decision makers (DMs), one of the basic approaches to built consensus is to aggregate individual evaluations or individual priorities. Thus, to use the hesitant fuzzy numbers for consensus building with a group of DMs, we further propose a method called maximizing score deviation method to obtain the DMs’ weights under the HPFE environment, based on which two extended and four new ordered weighted operators are provided to fuse the HPFE information and build the consensus of the DMs. We also analyze the differences among these ordered weighted operators and provide their application scopes. Finally, a practical case is provided to demonstrate consensus building with a group of DMs under the HPFE environment using the proposed approaches.
TL;DR: The power average operator can relieve the some influences of unreasonable data given by biased decision makers, and Heronian mean operator can consider the interrelationship of the aggregated arguments to take full advantages of these two kinds of operators.
TL;DR: In this article, a combination of sustainability and risk factors was considered for third-party reverse logistic provider (3PRLP) evaluation, and fuzzy step-wise weight assessment ratio analysis (Fuzzy SWARA) was applied for weighing the evaluation criteria.
Abstract: Third-party logistic provider (3PLP) companies play a major role in supply chain management (SCM) by carrying out specialized functions—namely, integrated operation, warehousing, and transportation services. Taking sustainability issues into consideration makes reverse logistics even more significant. In this paper, a combination of sustainability and risk factors was considered for third-party reverse logistic provider (3PRLP) evaluation. Initially, fuzzy step-wise weight assessment ratio analysis (Fuzzy SWARA) was applied for weighing the evaluation criteria; then, Fuzzy multi-objective optimization on the basis of ratio analysis (Fuzzy MOORA) was utilized for ranking the sustainable third-party reverse logistic providers in the plastic industry in the second step. Findings highlight that quality, recycling, health, and safety were the most important criteria in economic, environmental, and social dimensions of sustainability, respectively. Also, operational risk was found to have the highest weight among risk factors.
TL;DR: The proposed adaptive fuzzy controllers not only solve the “explosion of complexity” problem existing in conventional backstepping control schemes, but as well as avoid the calculation of partial derivatives.
Abstract: The adaptive fuzzy tracking control design problem for multi-input and multi-output uncertain switched nonstrict-feedback nonlinear systems with arbitrary switchings is investigated in this paper. Fuzzy logic systems are introduced to identify the unknown nonlinear functions (for state measurable case) and model the uncertain nonlinear systems (for state immeasurable case). Both state feedback and observer-based output feedback control design schemes are developed based on combined command filter and adaptive fuzzy control technique. The proposed adaptive fuzzy controllers not only solve the “explosion of complexity” problem existing in conventional backstepping control schemes, but as well as avoid the calculation of partial derivatives. Furthermore, the stability of the fuzzy control systems under arbitrary switchings is proven based on the common Lyapunov function method. Two simulation examples are presented to further demonstrate the effectiveness of the proposed control strategies.