Showing papers on "Fuzzy logic published in 2020"
TL;DR: A novel observer-based adaptive fuzzy output-feedback backstepping control method that can make the control system be semiglobally uniformly ultimately boundedness (SGUUB) and keep the observer and tracking errors to remain in a small neighborhood of the origin.
Abstract: This article investigates the adaptive fuzzy output-feedback backstepping control design problem for uncertain strict-feedback nonlinear systems in the presence of unknown virtual and actual control gain functions and unmeasurable states. A fuzzy state observer is designed via fuzzy-logic systems, thus the unmeasurable states are estimated based on the designed fuzzy state observer. By constructing the logarithm Lyapunov functions and incorporating the property of the fuzzy basis functions and bounded control design technique into the adaptive backstepping recursive design, a novel observer-based adaptive fuzzy output-feedback control method is developed. The proposed fuzzy adaptive output-feedback backstepping control scheme can remove the restrictive assumptions in the previous literature that the virtual control gains and actual control gain functions must be constants. Furthermore, it can make the control system be semiglobally uniformly ultimately boundedness (SGUUB) and keep the observer and tracking errors to remain in a small neighborhood of the origin. The numerical simulation example is presented to validate the effectiveness of the proposed control scheme and theory.
380 citations
TL;DR: The intermittent fault-tolerance scheme is taken into fully account in designing a reliable asynchronous sampled-data controller, which ensures such that the resultant neural networks is asymptotically stable.
313 citations
TL;DR: This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems.
Abstract: Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.
300 citations
TL;DR: Thorough experimental analysis shows that the adaptive genetic algorithm with fuzzy logic (AGAFL) model has outperformed current existing methods in diagnosing heart disease at early stages.
Abstract: For the past two decades, most of the people from developing countries are suffering from heart disease. Diagnosing these diseases at earlier stages helps patients reduce the risk of death and also in reducing the cost of treatment. The objective of adaptive genetic algorithm with fuzzy logic (AGAFL) model is to predict heart disease which will help medical practitioners in diagnosing heart disease at early stages. The model consists of the rough sets based heart disease feature selection module and the fuzzy rule based classification module. The generated rules from fuzzy classifiers are optimized by applying the adaptive genetic algorithm. First, important features which effect heart disease are selected by rough set theory. The second step predicts the heart disease using the hybrid AGAFL classifier. The experimentation is performed on the publicly available UCI heart disease datasets. Thorough experimental analysis shows that our approach has outperformed current existing methods.
274 citations
TL;DR: A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi–Sugeno (TS) fuzzy system into BLS, and the results indicate that fuzzy BLS outperforms other models involved.
Abstract: A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by merging the Takagi–Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature nodes of BLS with a group of TS fuzzy subsystems, and the input data are processed by each of them. Instead of aggregating the outputs of fuzzy rules produced by every fuzzy subsystem into one value immediately, all of them are sent to the enhancement layer for further nonlinear transformation to preserve the characteristic of inputs. The defuzzification outputs of all fuzzy subsystem and the outputs of enhancement layer are combined together to obtain the model output. The ${k}$ -means method is employed to determine the centers of Gaussian membership functions in antecedent part and the number of fuzzy rules. The parameters that need to be calculated in a fuzzy BLS are the weights connecting the outputs of enhancement layer to model output and the randomly initialized coefficients of polynomials in consequent part in fuzzy subsystems, which can be calculated analytically. Therefore, fuzzy BLS retains the fast computational nature of BLS. The proposed fuzzy BLS is evaluated by some popular benchmarks for regression and classification, and compared with some state-of-the-art nonfuzzy and neuro-fuzzy approaches. The results indicate that fuzzy BLS outperforms other models involved. Moreover, fuzzy BLS shows advantages over neuro-fuzzy models regarding to the number of fuzzy rules and training time, which can ease the problem of rule explosion to some extent.
254 citations
TL;DR: The problem of asymptotic tracking control for a class of uncertain switched nonlinear systems under fuzzy approximation framework is solved by constructing a nonsmooth Lyapunov function and introducing a novel discontinuous controller with dynamic feedback compensator in the design procedure.
Abstract: The problem of asymptotic tracking control for a class of uncertain switched nonlinear systems under fuzzy approximation framework is solved in this paper. Superior to most existing results based on fuzzy adaptive control strategy that can only achieve bounded error tracking performance, our proposed control scheme can guarantee the local asymptotic tracking performance for the uncertain switched nonlinear systems under consideration. This is accomplished by constructing a nonsmooth Lyapunov function and introducing a novel discontinuous controller with dynamic feedback compensator in the design procedure. Meanwhile, some concepts, such as differential inclusion and set-valued map, are introduced to theoretically verify the local asymptotic tracking performance of the systems with our proposed controller. With the help of set-valued Lie derivative, the common virtual control functions, the desired controller, and the adaptive laws can be precisely constructed. Finally, simulation results are given to show the effectiveness of the proposed method.
251 citations
TL;DR: A modified loose-looped fuzzy membership functions (FMFs) dependent Lyapunov-Krasovskii functional (LKF) is constructed based on the information of the time derivative of FMFs, which involves not only a signal transmission delay but also switched topologies.
246 citations
01 Mar 2020
TL;DR: The aim is to extend classical analytic hierarchy process (AHP) to spherical fuzzy AHP (SF-AHP), and to show its applicability and validity through a renewable energy location selection example and a comparative analysis between neutrosophic AHP and SF-A HP.
Abstract: The extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, and neutrosophic sets, whose membership functions are based on three dimensions, aim at collecting experts’ judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been introduced by Kutlu Gundogdu and Kahraman (J Intell Fuzzy Syst 36(1):337–352, 2019a), including their arithmetic operations, aggregation operators, and defuzzification operations. In this paper, our aim is to extend classical analytic hierarchy process (AHP) to spherical fuzzy AHP (SF-AHP) method and to show its applicability and validity through a renewable energy location selection example and a comparative analysis between neutrosophic AHP and SF-AHP.
222 citations
TL;DR: This paper considers the problem of unknown gains and input quantization, which can be addressed by using a lemma and Nussbaum function in cooperative control, and fuzzy logic systems are proposed to approximate the nonlinear function defined on a compact set.
Abstract: This paper studies the quantized cooperative control problem for multiagent systems with unknown gains in the prescribed performance. Different from the finite-time control, a speed function is designed to realize that the tracking errors converge to a prescribed compact set in a given finite time for multiagent systems. Meanwhile, we consider the problem of unknown gains and input quantization, which can be addressed by using a lemma and Nussbaum function in cooperative control. Moreover, the fuzzy logic systems are proposed to approximate the nonlinear function defined on a compact set. A distributed controller and adaptive laws are constructed based on the Lyapunov stability theory and backstepping method. Finally, the effectiveness of the proposed approach is illustrated by some numerical simulation results.
216 citations
TL;DR: The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness.
Abstract: This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples.
210 citations
TL;DR: A novel hybrid approach based on the fuzzy logic is implemented to address the sustainable supplier selection problem and Weighted Goal Programming (WGP) method is used to deal with multi-objectiveness.
TL;DR: The proposed framework makes it possible to evaluate suppliers in terms of sustainability in spite of ambiguities in the decision-making process and a lack of quantitative information.
TL;DR: These new Pythagorean fuzzy interaction PBM operators can capture the interactions between the membership and nonmembership function of PFNs and retain the main merits of the PBM operator.
Abstract: The power Bonferroni mean (PBM) operator can relieve the influence of unreasonable aggregation values and also capture the interrelationship among the input arguments, which is an important generalization of power average operator and Bonferroni mean operator, and Pythagorean fuzzy set is an effective mathematical method to handle imprecise and uncertain information. In this paper, we extend PBM operator to integrate Pythagorean fuzzy numbers (PFNs) based on the interaction operational laws of PFNs, and propose Pythagorean fuzzy interaction PBM operator and weighted Pythagorean fuzzy interaction PBM operator. These new Pythagorean fuzzy interaction PBM operators can capture the interactions between the membership and nonmembership function of PFNs and retain the main merits of the PBM operator. Then, we analyze some desirable properties and particular cases of the presented operators. Further, a new multiple attribute decision making method based on the proposed method has been presented. Finally, a numerical example concerning the evaluation of online payment service providers is provided to illustrate the validity and merits of the new method by comparing it with the existing methods.
TL;DR: This article proposes a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results and shows that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature.
Abstract: The emergence of fuzzy sets makes job-shop scheduling problem (JSSP) become better aligned with the reality. This article addresses the JSSP with fuzzy execution time and fuzzy completion time (FJSSP). We choose the classic differential evolution (DE) algorithm as the basic optimization framework. The advantage of the DE algorithm is that it uses a special evolutionary strategy of difference vector sets to carry out mutation operation. However, DE is not very effective in solving some instances of FJSSP. Therefore, we propose a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results. The proposed selection operator adopted in this article aims at a temporary retention of all children generated by the parent generation, and then selecting N better solutions as the new individuals from N parents and N children. Various examples of fuzzy shop scheduling problems are experimented with to test the performance of the improved DE algorithm. The NSODE algorithm is compared with a variety of existing algorithms such as ant colony optimization, particle swarm optimization, and cuckoo search. Experimental results show that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature.
TL;DR: A novel evidential fuzzy MCDM method, called EFMCDM, is proposed by integrating Dempster–Shafer theory with belief entropy to decrease the uncertainty caused by subjective human cognition to improve decision making.
Abstract: Multicriteria decision making (MCDM) has become one of the most frequently applied decision making methodologies in various fields. However, uncertainty is inevitably involved in the process of MCDM due to the subjectivity of humans. To address this issue, a novel evidential fuzzy MCDM method, called EFMCDM, is proposed by integrating Dempster–Shafer theory with belief entropy. In particular, each criterion can be modeled as evidence, and all the alternatives compose the frame of discernment in the framework of Dempster–Shafer theory. To generate more appropriate basic probability assignments (BPAs) of the criteria, the EFMCDM method considers both the subjective and objective weighting of the criteria that are leveraged in MCDM problems. Thereafter, the classic Dempster's rule of combination is leveraged to fuse the multiple pieces of evidence into composite evidence. On this basis, the alternatives are ranked to determine the optimal alternative. In addition, the EFMCDM method can quantitatively model uncertainty and help to decrease the uncertainty caused by subjective human cognition to improve decision making. Finally, the rationality, effectiveness, and robustness of the EFMCDM method are demonstrated through experimental evaluations.
TL;DR: A novel picture fuzzy multi-criteria decision making (MCDM) model is proposed to solve the site selection problem for car sharing stations in Bejing and the definition of picture fuzzy sets is adopted to accurately portray the voting information.
TL;DR: A hybrid approach of fuzzy analysis network process, fuzzy decision-making trial and evaluation laboratory, and multi-objective mixed-integer linear programming models are developed for circular supplier selection and order allocation in a multi-product circular closed-loop supply chain (C-CLSC).
TL;DR: A fuzzy sliding-mode controller is developed to realize reachability of a predefined switching surface and desirable sliding motion and sufficient conditions for stochastic stability of the obtained sliding mode dynamics is developed in the sense of generally uncertain transition rates.
Abstract: This paper is focused on the event-triggered fuzzy sliding-mode control of networked control systems regulated by semi-Markov process. First, through movement-decomposition method, the networked control system is transformed into two lower-order subsystems. Then, an event-triggered scheme based on a delay system model approach is proposed in designing the switching surface and obtaining the sliding mode dynamics. Furthermore, a fuzzy sliding-mode controller is developed to realize reachability of a predefined switching surface and desirable sliding motion. Moreover, in terms of linear matrix inequality method, sufficient conditions for stochastic stability of the obtained sliding mode dynamics is developed in the sense of generally uncertain transition rates. Finally, the applicability of the proposed results are verified numerically on the single-link robot arm system.
TL;DR: A globally stable adaptive fuzzy backstepping control design is proposed for nonlinear bilateral teleoperation manipulators to handle the aforementioned issues of communication delay, nonlinearities, and uncertainties.
Abstract: Bilateral teleoperation technology has been widely concerned by its unique advantages in human–machine interaction-based cooperative operation systems. Communication delay, various nonlinearities, and uncertainties in teleoperation system are the main challenging issues to achieve system stability and good transparency performance. In this paper, a globally stable adaptive fuzzy backstepping control design is proposed for nonlinear bilateral teleoperation manipulators to handle the aforementioned issues. For the communication channel, instead of direct transmission of environmental torque signals, the fuzzy-based nonpower approximate environmental parameters are transmitted to the master side for environmental torque prediction, which effectively avoids the transmission of power signals in the delayed communication channel and solves the passivity problem in the traditional teleoperation system. A trajectory generator is implemented in the master side and a trajectory smoothing is applied in the slave side. Subsequently, nonlinear adaptive fuzzy backstepping controllers for master and slave are separately designed to handle the nonlinearities and uncertainties. Theoretically, the great transparency performance of both position tracking and force feedback can be achieved, and the global stability is still guaranteed under communication delay. Comparative experiments are conducted on the real platform, which verify the effectiveness and advantages of the proposed control design in some typical working scenarios.
TL;DR: This article studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach and it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with $H_{\infty }$ performance.
Abstract: This article studies the fault detection problem for continuous-time fuzzy semi-Markov jump systems (FSMJSs) by employing an interval type-2 (IT2) fuzzy approach. First, the continuous-time FSMJSs model is designed and the parameter uncertainty is addressed by the IT2 fuzzy approach, where the characteristic of sensor saturation is taken into account in the control system. Second, the IT2 fuzzy semi-Markov mode-dependent filter is constructed, which is employed to deal with the fault detection problem. Then, by using the Lyapunov theory, it can be guaranteed that the constructed fault detection model based on this filter and IT2 FSMJSs is stochastically stable with $H_{\infty }$ performance. Moreover, the quantization strategy is applied to the fault detection plant to dispose of the problem of limited network bandwidth. Compared with the existing literature, the differences mainly lie in two aspects, one is that the IT2 fuzzy method is utilized for FSMJSs to tackle the parameter uncertainty of system, and the other is to detect the fault signal of IT2 FSMJSs by using the fault detection system that is constructed based on the IT2 fuzzy semi-Markov mode-dependent filter and IT2 FSMJSs. Finally, two simulation examples are provided to illustrate the effectiveness and the usefulness of the proposed theoretical method.
01 Aug 2020
TL;DR: A new definition of fuzzy fractional derivative, so-called fuzzy conformable, is proposed and the reproducing kernel Hilbert space method in the conformable emotion is constructed side by side with numerical results, tabulated data, and graphical representations.
Abstract: The aim of this article is to propose a new definition of fuzzy fractional derivative, so-called fuzzy conformable. To this end, we discussed fuzzy conformable fractional integral softly. Meanwhile, uniqueness, existence, and other properties of solutions of certain fuzzy conformable fractional differential equations under strongly generalized differentiability are also utilized. Furthermore, all needed requirements for characterizing solutions by equivalent systems of crisp conformable fractional differential equations are debated. In this orientation, modern trend and new computational algorithm in terms of analytic and approximate conformable solutions are proposed. Finally, the reproducing kernel Hilbert space method in the conformable emotion is constructed side by side with numerical results, tabulated data, and graphical representations.
TL;DR: This paper focuses on three-way conflict analysis based on the Bayesian minimum risk theory and explores examples to show how to compute the positive, neutral, and negative alliances with a Pythagorean fuzzy loss function given by an expert.
Abstract: In some real-world situations, Pythagorean fuzzy sets are more powerful and effective than intuitionistic fuzzy sets to describe vague and uncertain information, and there are many Pythagorean fuzzy information systems for conflicts in which attitudes of agents on issues are depicted by Pythagorean fuzzy numbers. In this paper, we first provide the concepts of positive, neutral, and negative alliances with two thresholds and employ examples to illustrate how to compute positive, neutral, and negative alliances in Pythagorean fuzzy information systems for conflicts. Then, we focus on three-way conflict analysis based on the Bayesian minimum risk theory and explore examples to show how to compute the positive, neutral, and negative alliances with a Pythagorean fuzzy loss function given by an expert. Finally, we study how to calculate positive, neutral, and negative alliances with group decision theory and take examples to demonstrate how to construct the positive, neutral, and negative alliances with a group of Pythagorean fuzzy loss functions given by more experts.
TL;DR: An overview of the most relevant work in the area of type-2 fuzzy logic, including its theoretical and practical implications, as well as envisioning possible future works and trends in this area of research.
24 Mar 2020
TL;DR: A new fuzzy multi-criteria decision-making model for traffic risk assessment was developed and there is a dominant section with the highest risk for all road participants, which requires corrective actions.
Abstract: In this paper, a new fuzzy multi-criteria decision-making model for traffic risk assessment was developed. A part of a main road network of 7.4 km with a total of 38 Sections was analyzed with the aim of determining the degree of risk on them. For that purpose, a fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS) method was developed. In addition, a new fuzzy linguistic scale quantified into triangular fuzzy numbers (TFNs) was developed. The fuzzy PIvot Pairwise RElative Criteria Importance Assessment—fuzzy PIPRECIA method—was used to determine the criteria weights on the basis of which the road network sections were evaluated. The results clearly show that there is a dominant section with the highest risk for all road participants, which requires corrective actions. In order to validate the results, a comprehensive validity test was created consisting of variations in the significance of model input parameters, testing the influence of dynamic factors—of reverse rank, and applying the fuzzy Simple Additive Weighing (fuzzy SAW) method and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS). The validation test show the stability of the results obtained and the justification for the development of the proposed model.
TL;DR: A novel framework is elaborated which combines AHP and TOPSIS with a spherical fuzzy set, which is effective in handling uncertainty in decision making and leads to robust and competitive results compared with state-of-the-art multi-criteria decision-making (MCDM) approaches.
TL;DR: It is concluded that the proposed risk analysis provides a deep insight on risk control, especially for complex project environment, which enables to not only reduce the likelihood of failure ahead of time but also mitigate risk magnitudes to some degree after the occurrence of a failure.
Abstract: A novel risk analysis approach is developed by merging interval-valued fuzzy sets (IVFSs), improved Dempster–Shafer (D–S) evidence theory, and fuzzy Bayesian networks (BNs), acting as a systematic decision support approach for safety insurance for the entire life cycle of a complex system under uncertainty. Aiming to alleviate the problem of insufficient and imprecise data collected from the complicated environment, the expert judgment in linguistic expressions is employed to describe the risk levels for all risk factors, which are represented by IVFSs using Gaussian membership function to fully consider such fuzziness and uncertainty. In regard to interval fusion and highly conflicting data, an improved combination rule based on the D–S evidence theory is developed. Then, fuzzy prior probability for each risk factor can be generated from fused intervals and fed into a fuzzy BN model for fuzzy-based Bayesian inference, including predictive, sensitivity, and diagnosis analysis. Furthermore, a case study is used to demonstrate the feasibility of the proposed risk analysis. A comparison of risk analysis based upon the hybrid improved D–S, classical D–S, and arithmetic average method is illustrated to show the outstanding performance of the developed approach in fusing multisource information with ubiquitous uncertainty and conflicts in an efficient manner, leading to more reliable risk evaluation. It is concluded that the proposed risk analysis provides a deep insight on risk control, especially for complex project environment, which enables to not only reduce the likelihood of failure ahead of time but also mitigate risk magnitudes to some degree after the occurrence of a failure.
TL;DR: The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set and the prediction errors are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.
TL;DR: An incremental online identification algorithm is applied to develop a set of evolving fuzzy models that characterize the nonlinear finger dynamics of the human hand for the myoelectric (ME)-based control of a prosthetic hand to show the effectiveness of the PI-fuzzy controllers and the performance improvement in comparison to the initial PI ones.
Abstract: This article applies an incremental online identification algorithm to develop a set of evolving fuzzy models (FMs) that characterize the nonlinear finger dynamics of the human hand for the myoelectric (ME)-based control of a prosthetic hand. The FM inputs are the ME signals obtained from eight ME sensors and past inputs and/or outputs. The FM outputs are the finger angles, considered here as the midcarpal joint angles, to ensure their control. The best evolving FMs that characterize each of the five fingers are described with the results validated on real data. Simple second-order linear models are next given to enable the cost-effective controller design. Five separate control loops are proposed, with proportional–integral (PI) controllers separately tuned by a frequency-domain approach. Simple PI-fuzzy controllers are designed starting with the linear PI controllers to ensure the control system performance improvement. The evolving FMs are used to simulate accurately the behavior of the human hand. Digital simulation results are included to show the effectiveness of the PI-fuzzy controllers and the performance improvement in comparison to the initial PI ones.
01 Feb 2020
TL;DR: The hybrid method resulting from combining the Intuitionistic Fuzzy Set and TOPSIS is very effective to select which supplier is more suitable among the alternatives and also this method can be integrated to similar problems.
Abstract: One of the most important functions of supply chain management is to enhance competitive pressure. Competition conditions and customer perception have changed in favor of environmentalist attitude. Therefore, green supplier selection (GSS) has become an important issue. In this study, the problem of GSS aiming for lean, agile, environmentally sensitive, sustainability, and durability is addressed. The environmental criteria considered in GSS and classical supplier selection are different from each other in terms of carbon footprint, water consumption, environmental applications, and recycling applications. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method has been used in the problem of GSS by considering the multi-criteria decision-making (MCDM) method since MCDM is very effective in many aspects such as evaluating and selecting the classical and environmental criteria. Due to linguistic criteria and no possibility to measure all criteria, it is needed to consolidate the fuzzy approach with the TOPSIS method to reduce the effects of ambiguity and instability. The Intuitionistic Fuzzy TOPSIS method is used because this method makes evaluating decision-makers and criteria convenient. According to the criteria determined by the order of importance, the hybrid method resulting from combining the Intuitionistic Fuzzy Set and TOPSIS is very effective to select which supplier is more suitable among the alternatives and also this method can be integrated to similar problems.
01 Feb 2020
TL;DR: Experimental results show the proposed method CFCSA is better against comparative models in feature selection on the medical diagnosis data sets.
Abstract: Powerful knowledge acquisition tools and techniques have the ability to increase both the quality and the quantity of knowledge-based systems for real-world problems. In this paper, we designed a hybrid crow search optimization algorithm integrated with chaos theory and fuzzy c-means algorithm denoted as CFCSA for feature selection problems of medical diagnosis. In the proposed CFCSA framework, the crow search algorithm adopts the global optimization technique to avoid the sensitivity of local optimization. The fuzzy c-means (FCM) objective function is used as a cost function for the chaotic crow search optimization algorithm. The proposed algorithm CFCSA is benchmarked against the binary crow search algorithm (BCSA), chaotic ant lion optimization algorithm (CALO), binary ant lion optimization algorithm (BALO) and bat algorithm relevant methods. The proposed CFCSA algorithm vs. BCSA, CALO, BALO and bat algorithm is tested on diabetes, heart, Radiopaedia CT liver, breast cancer, lung cancer, cardiotocography, ILPD, liver disorders, hepatitis and arrhythmia. Experimental results show the proposed method CFCSA is better against comparative models in feature selection on the medical diagnosis data sets.