Showing papers in "International Journal of Fuzzy Systems in 2017"

A Bibliometric Analysis of Fuzzy Decision Research During 1970–2015

Abstract: Fuzzy set just past its 50-year anniversary and different fuzzy associations and organizations hold different forms of conferences and activities to celebrate this epoch-making scientific discovery As an important branch of fuzzy theory, fuzzy decision has attracted scholars from almost all fields from psychologists, economists, to computer scientists In this paper, we conduct a bibliometric analysis on fuzzy decision-related research to find out some underlying patterns and dynamics in this research direction A total of 13,901 fuzzy decision-related publication records from Web of Science are analyzed with the aid of the text-mining software Vantage Point Many interesting results with regard to the annual trends, the top players in terms of country level, time dynamic as well as institutional level, the publishing journals, the highly cited papers, and the research landscape are yielded and explained in-depth It is observed that some small or developing economies (such as China, Iran, Taiwan, and Turkey) are quite active in fuzzy decision research The fuzzy decision theories and methods have increasingly be utilized in various fields evidenced by the growing number of disciplines involved in the fuzzy decision research

Picture 2-Tuple Linguistic Bonferroni Mean Operators and Their Application to Multiple Attribute Decision Making

Abstract: In this paper, we investigate the multiple attribute decision-making problems with picture 2-tuple linguistic information. Then, we utilize Bonferroni mean and geometric Bonferroni mean operations to develop some picture 2-tuple linguistic aggregation operators: picture 2-tuple linguistic Bonferroni mean operator and picture 2-tuple linguistic geometric Bonferroni mean operator. Some desired properties and special cases of the developed operators are discussed in detail. Furthermore, considering the importance of the input arguments, we propose the picture 2-tuple linguistic weighted Bonferroni mean operator and picture 2-tuple linguistic weighted geometric Bonferroni mean operator. Finally, a practical example for selecting the service outsourcing provider of communications industry is given to verify the developed approach and to demonstrate its practicality and effectiveness.

An Interval Type-2 Fuzzy Likelihood-Based MABAC Approach and Its Application in Selecting Hotels on a Tourism Website

Abstract: As an extension of type-1 fuzzy sets (T1FSs), interval type-2 fuzzy sets (IT2FSs) can be used to model both extrinsic and intrinsic uncertainties. Based on the likelihood of interval type-2 fuzzy numbers (IT2FNs), this paper proposes a new multi-attributive border approximation area comparison (MABAC) approach to solve multi-criteria decision-making (MCDM) problems. First, an algorithm to decompose IT2FNs into the embedded type-1 fuzzy numbers (T1FNs) is proposed. Second, based on the closeness degree of T1FNs, the likelihood of IT2FNs is defined using the decomposition algorithm, and related properties are discussed. Third, the total ranking of alternatives is obtained using the MABAC approach based on the likelihood of IT2FNs. Finally, a practical example of selecting hotels from a tourism website is presented to verify the validity and feasibility of the proposed approach. A comparative analysis with existing methods is also described.

A Linear Assignment Method for Multiple Criteria Decision Analysis with Hesitant Fuzzy Sets Based on Fuzzy Measure

Abstract: The aim of this paper is to develop an approach to solve the multiple criteria decision making (MCDM) problems under the hesitant fuzzy environment, in which the criteria values take the form of the hesitant fuzzy elements (HFEs) and the information about criteria weights are correlative. Based on the λ-fuzzy measure, we firstly get the weight vector of the criteria. Secondly, we propose the linear assignment method to acquire the optimal preference ranking of the alternatives according to a set of criteria-wise rankings and a set of criteria importance within the context of HFEs on the basis of the Hesitant Euclidean distance. Finally, a practical example about talent introduction is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Hesitant Uncertain Linguistic Z-Numbers and Their Application in Multi-criteria Group Decision-Making Problems

Abstract: This paper introduces hesitant uncertain linguistic Z-numbers (HULZNs) based on Z-numbers and linguistic models. HULZNs can serve as a reliable tool to depict complex and uncertain decision-making information and reflect the hesitancy of DMs. This paper focuses on the development of an innovative method to address multi-criteria group decision-making (MCGDM) problems in which the weight information is incompletely known. Handling qualitative information requires the effective support of quantitative tools, after which the linguistic scale function is employed to deal with linguistic information. First, the operations and distance of HULZNs are defined. Then, two power aggregation operators for HULZNs are proposed. Subsequently, a new MCGDM approach is developed by incorporating the power aggregation operators and the VIKOR model. Finally, an illustrative example of ERP system selection is provided for demonstration, and the feasibility and validity of the proposed method are further verified by sensitivity analysis and comparison with an existing method.

An Extended QUALIFLEX Method Under Probability Hesitant Fuzzy Environment for Selecting Green Suppliers

Abstract: For many companies, the selection of green suppliers has become a key strategic consideration due to regulatory requirements and market trends. In this paper, an extended qualitative flexible multiple (QUALIFLEX) method is used to solve problems regarding the selection of green suppliers using probability hesitant fuzzy information. First, a new Hausdorff distance between two probability hesitant fuzzy elements (PHFEs) is introduced, and the properties of the proposed distance are discussed. Second, a likelihood measurement for PHFEs is proposed based on the new Hausdorff distance; moreover, an extended QUALIFLEX is developed based on the likelihood measurement. Finally, the practicality and effectiveness of the proposed method is tested using a green supplier selection example; furthermore, a comparative analysis using probability hesitant fuzzy information is also performed.

Imperialist Competitive Algorithm-Based Optimization of Neuro-Fuzzy System Parameters for Automatic Red-eye Removal

Abstract: There are great deals of consumer photographs which are affected by red-eye artifacts and arise frequently when shooting with flash. In this paper, a new technique is proposed to solve this problem. The proposed technique starts by detecting the skin-like regions using an optimized pixel-based neuro-fuzzy processing; morphological operations are then used to discard the extra areas after crossing the threshold. Once the skin regions are detected, five new features including geometric and color metrics are proposed to enhance the classification accuracy of the red-eye artifacts. After that, another optimized neuro-fuzzy classifier is employed to classify the red-eye regions by using the presented features. Final result is achieved by a definite syntax between skin and red-eye regions, and then, a simple correction method is used to correct the detected regions. Finally, a comparison is performed among the proposed method toward the other popular procedures and also a simple neuro-fuzzy. Final results showed the high performance of the proposed method.

A Dynamic Reference Point Method for Emergency Response Under Hesitant Probabilistic Fuzzy Environment

Abstract: According to the characteristics of emergency decision-making in crisis management, this paper proposes a dynamic decision-making method using the hesitant probabilistic fuzzy set to deal with the inadequate information, uncertainty and dynamic trends. This method is suitable for emergency decision-making as it provides supports for the dynamic and evolutionary characteristics of emergency responses and the uncertain probability about external environment is also considered. In order to make a continuous adjustment with the development of situations, we give a definition of the expectation level, based on which the dynamic reference point method is proposed to obtain the optimal emergency response plan under the hesitant probabilistic fuzzy environment. We also analyze the probability of different situations that may occur in the process of emergency decision-making and provide an algorithm for solving this problem. Finally, a practical case of hazardous goods leakage pollution accident is given to illustrate our method, and then, the optimal decision alternative chain is obtained.

Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System

Abstract: With the development of society, although the way that people get information more and more convenient, the information which people get may be incomplete and has a little degree of uncertainty and fuzziness. In real life, the incomplete fuzzy phenomenon of information source exists widely. It is extremely meaningful to fuse multiple fuzzy incomplete information sources effectively. In this study, a new method is presented for information fusion based on information entropy in fuzzy incomplete information system and the effectiveness of the new method is verified by comparing the average fusion method. Then, an illustrative example is delivered to illustrate the effectiveness of the proposed fusion method. Finally, we have also tested the veracity and validity of this method by experiment on a dataset from UCI. The results of this study will be useful for pooling the uncertain data from different information sources and significant for establishing a distinct direction of the fusion method.

Hesitant Fuzzy Multiattribute Matching Decision Making Based on Regret Theory with Uncertain Weights

Abstract: An approach based on regret theory with hesitant fuzzy analysis is presented in a context of multiattribute matching decision making where the relative weights are uncertain. There are two steps being addressed in this approach. First, we put forward a maximizing differential model to determine the relative weights of hesitant fuzzy attributes, and calculate collective utilities of each attribute according to regret theory. The matching satisfaction degrees (MSDs) are then acquired by aggregating the collective utilities with relative weights. Secondly, an optimal matching model is programmed to generate the matching results based on the MSDs. This model belongs to a sort of multiobjective assignment problem and can be solved using the min–max method. A case study of matching outsourcing contractors and providers in Fuzhou National Hi-tech Zone is conducted to demonstrate the proposed approach and its potential applications.

Exponential Intuitionistic Fuzzy Information Measure with Assessment of Service Quality

Abstract: In the present paper, an exponential intuitionistic fuzzy information measure is proposed. The consistency of the proposed measure over existing information measures is illustrated mathematically. The importance of service quality grading is apparent with increasing demand to meet the customer needs in highly competitive service-related industry. However, it is not always straightforward as the constraints in grading processes and customer perceptions towards services are intangible measures. This paper presents the grading for service quality of four vehicle insurance companies using intuitionistic fuzzy weighted information measure. The IFWIM is useful to represent the decision information in the process of decision-making since it was characterized by degrees of membership, non-membership and hesitation. The crisp survey results were collected via questionnaires from customers of the selected region and analysed using the IFWIM. These grading results would be useful for insurance companies in upgrading their service quality and eventually able to fulfil customers’ needs.

PSO-Self-Organizing Interval Type-2 Fuzzy Neural Network for Antilock Braking Systems

Abstract: Nowadays, the antilock braking system (ABS) is the standard in all modern cars. The function of ABS is to optimize the maximize wheel traction by preventing wheel lockup during braking, so it can help the drivers to maintain steering maneuverability. In this study, a self-organizing interval type-2 fuzzy neural network (SOT2FNN) control system is designed for antilock braking systems. This control system comprises a main controller and a robust compensation controller; the SOT2FNN as the main controller is used to mimic an ideal controller, and the robust compensation controller is developed to eliminate the approximation error between the main controller and the ideal controller. To guarantee system stability, adaptive laws for adjusting the parameters of SOT2FNN based on the gradient descent method are proposed. However, in control design, the learning rates of adaptive law are very important and they significantly affect control performance. The particle swarm optimization method is therefore applied to find the optimal learning rates for the weights in reduction layer and also for the means, the variances of the Gaussian functions in the input membership functions. Finally, the numerical simulations of ABS response in different road conditions are provided to illustrate the effectiveness of the proposed approach.

Personalized Adaptive Learner Model in E-Learning System Using FCM and Fuzzy Inference System

Abstract: Each learner has unique learning style in which one learns easily. It is aimed to individualize the learning experiences for each learner in e-learning. Therefore, it is important to diagnose complete learners’ learning style and behaviour to provide suitable learning paths and automated personalized contents as per their choices. This paper proposes some new dimensions of adaptivity like automatic and dynamic detection of learning styles and provides personalization accordingly. It has advantages in terms of precision and time spent. It is a literature-based approach in which a personalized adaptive learner model (PALM) was constructed. This proposed learner model mines learner’s navigational accesses data and finds learner’s behavioural patterns which individualize each learner and provide personalization according to their learning styles in the learning process. Fuzzy cognitive maps and fuzzy inference system a soft computing techniques were introduced to implement PALM. Result shows that personalized adaptive e-learning system is better and promising than the non-adaptive in terms of benefits to the learners and improvement in overall learning process. Thus, providing adaptivity as per learner’s needs is an important factor for enhancing the efficiency and effectiveness of the entire learning process.

Fuzzy Soft Expert System in Prediction of Coronary Artery Disease

Abstract: Coronary artery disease affects millions of people all over the world including a major portion in Egypt every year. Although much progress has been done in medical science, early detection of this disease is still a challenge for prevention. In this paper we, will extend the concept of fuzzy soft set theory so as to develop a knowledge-based system in medicine and devise a prediction system named fuzzy soft expert system consisting of four main components. These are a fuzzification which translates inputs into fuzzy values, fuzzification of data sets to obtain fuzzy soft sets, a new fuzzy soft set by normal parameter reduction of fuzzy soft set and an algorithm to produce the resultant output. The fuzzy soft expert system developed is then used to predict for coronary artery disease using systolic blood pressure, low-density lipoprotein cholesterol, maximum heart rate, blood sugar, old peak and age of patients. A preliminary study is conducted on nine male patients undergoing treatment in the Cardiac Unit of the Faculty of Medicine, Assiut University, Egypt. It is found that the fuzzy soft expert system developed is able is to help the expert doctor to decide whether a patient needs to be given drug therapy or intervention.

A New Method for Triangular Fuzzy Compare Wise Judgment Matrix Process Based on Consistency Analysis

Abstract: To cope with the uncertainty in the process of decision making, fuzzy preference relations are proposed and commonly applied in many fields. In practical decision-making problems, the decision maker may use triangular fuzzy preference relations to express his/her uncertainty. Based on the row weighted arithmetic mean method, this paper develops an approach for deriving the fuzzy priority vector from triangular fuzzy compare wise judgment matrices. To do this, this paper first analyzes the upper and lower bounds of the triangular fuzzy priority weight of each alternative, which indicates the decision maker’s optimistic and pessimistic attitudes. Based on (acceptably) consistent multiplicative preference relations, the triangular fuzzy priority vector is obtained. Meanwhile, a consistency concept of triangular fuzzy compare wise judgment matrices is defined, and the consistent relationship between triangular fuzzy and crisp preference relations is studied. Different to the existing methods, the new approach calculates the triangular fuzzy priority weights separately. Furthermore, the fuzzy priority vector from trapezoidal fuzzy reciprocal preference relations is considered. Finally, the application of the new method to new product development (NDP) project screening is tested, and comparative analyses are also offered.

Evidence-Based Trust Evaluation System for Cloud Services Using Fuzzy Logic

Abstract: Service interactions in cloud computing usually take place in an anonymous environment making trust management a non-trivial aspect. The increased use of cloud services in today’s highly computerized world unfolds an increasing need for trust management in cloud computing. The existing trust management systems which portrays a static trust relationship falls deficit when it comes to meeting up with the dynamic nature of cloud services. A dynamic evidence-based trust model is proposed to ascertain dynamic trustworthiness on services in the cloud environment. The trust model employs fuzzy logic to derive trust value in order to handle the uncertainty and uses induced ordered weight averaging operator to aggregate the trust values, thus enabling the real-time performance. The system makes use of QoS parameters as a substantiation to evaluate the trust for cloud services. The results in terms of efficiency and effectiveness of the model are demonstrated through simulations.

EEG Classification of Imaginary Lower Limb Stepping Movements Based on Fuzzy Support Vector Machine with Kernel-Induced Membership Function

Abstract: Although various kinds of motor imageries have been used for BCI applications, imaginary lower limb stepping movement has not been studied yet. The purpose of this study is to investigate the possibilities of using electroencephalography (EEG) signal to classify imaginary lower limb stepping movements and to design a robust motor imagery classifier based on support vector machine (SVM). A cue-based experimental paradigm is designed to record nine-channel EEG associated with imaginary left leg stepping (L-stepping) and right leg stepping (R-stepping) movements from eight healthy subjects. Features including band powers (BPs), common spatial pattern (CSP), and a filter-bank CSP (FB-CSP) were extracted from the recorded EEG. Fuzzy SVM (FSVM) is introduced to this study to classify L-stepping and R-stepping imageries. We propose a novel kernel-induced membership function to address the issue of data relative importance assignment. The FSVM with the membership function suggested in the original work of FSVM (Type-I FSVM) and the FSVM with the one we proposed (Type-II FSVM) is compared. Results indicated that the classification accuracies based on BP features are near the chance level (~50 %). Both alpha-band CSP (71.25 %) and FB-CSP (75.63 %) gave acceptable results as a simple k-NN classifier is performed. Results show that both types of FSVM performed better than the conventional SVM. Also, Type-II FSVM outperforms Type-I FSVM, especially when the alpha-CSP feature is employed, where the improvement in error reduction rate is over 15 %. The highest average L-stepping versus R-stepping classification accuracy over the eight subjects is achieved (86.25 % in single-trial analysis) by FB-CSP and FSVM-II. The high classification result suggests the feasibility of using lower limb stepping imagery to develop a BCI that can control devices or might be able to serve as a neurofeedback tool for users who need lower limb stepping imagery training for gait function improvement.

Tune Up Fuzzy C-Means for Big Data: Some Novel Hybrid Clustering Algorithms Based on Initial Selection and Incremental Clustering

Abstract: Data are getting larger, and most of them are necessary for our businesses. Rapid explosion of data brings us a number of challenges relating to its complexity and how the most important knowledge can be captured in reasonable time. Fuzzy C-means (FCM)—one of the most efficient clustering algorithms which have been widely used in pattern recognition, data compression, image segmentation, computer vision and many other fields—also faces the problem of processing large datasets. In this paper, we propose some novel hybrid clustering algorithms based on incremental clustering and initial selection to tune up FCM for the Big Data problem. The first algorithm determines meshes of rectangle covering data points as the representatives, while the second one considers data points that have high influence to others as the representatives. The representatives are then clustered by FCM, and the new centers are selected as initial ones for clustering of the dataset. Theoretical analyses of the new algorithms including comparison of quality of solutions when clustering the representatives set versus the entire set are examined. The experimental results on both simulated and real datasets show that total computational time of the new methods including time of finding representatives and clustering is faster than those of other relevant algorithms. The validation on clustering quality is also examined. The findings of this paper have great impact and significance to researches in the fields of soft computing and Big Data processing. It is obvious that computing methodologies nowadays are facing with huge amount of diverse and complex data structures. Speed of processing is the main priority when considering effectiveness of a specific method. The findings demonstrated practical algorithms and investigated their characteristics that could be referenced by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

Design and Optimization of Interval Type-2 Fuzzy Logic Controller for Delta Parallel Robot Trajectory Control

Abstract: In the view of the problem of designing and optimization of interval type-2 fuzzy logic controller (IT2 FLC) for Delta robot trajectory control, a systematic design method is put forward in this paper. A type-1 fuzzy logic controller (T1 FLC) is designed and optimized. Then, three kinds of method to blur the T1 fuzzy membership functions are proposed to generate IT2 fuzzy sets from the optimized T1 fuzzy sets. A systematic analysis is carried out to study the relationship between blur methods, blur degree and output control surface of IT2 FLC. Output signal enhance coefficient is proposed to make sure the IT2 FLC to provide enough output signal. The optimized IT2 FLC is validated through a set of simulations and by comparing against its type-1 counterpart in the presence of external and internal uncertainties. The simulation results show the optimized IT2 FLC can provide better trajectory tracking performance.

Selection of Membership Functions Based on Fuzzy Rules to Design an Efficient Power System Stabilizer

Abstract: This paper presents an evaluation of membership functions on a single-machine infinite-bus and two-area four-machine ten-bus power system with power system stabilizers (PSSs). The PSS is added to an excitation system to enhance the damping during low-frequency oscillations. In this paper, the system is analysed for fuzzy logic power system stabilizer (FPSS) with different membership functions (MFs). The speed deviation ( $$\Delta \omega$$ ) and acceleration ( $$\Delta \dot{\omega }$$ ) of the rotor of a synchronous generator are taken as the input to the fuzzy logic controller to improve small signal stability by enhancing damping. The effect of these variables on damping at the generator shaft mechanical oscillation is very significant. The stabilizing signals were computed using the different fuzzy membership functions in the Mamdani inference system. The general membership functions under consideration are triangular, trapezoidal, Gaussian, bell, sigmoid and polynomial types. The performance of the fuzzy logic PSS with different membership functions is compared to get best-suited MF to design an FPSS. The best-performing MF is found based on simulation study with both power systems and with varying no. of linguistic variables as well. It is found that, If the number of linguistic variables is 3 or 5, the preferred best-suited membership function appears as the Gaussian type, while with increased linguistic variables as 7 or above, then the triangular MF is preferable as the performance is better in comparison with Gaussian MF.

An Efficient Fuzzy-Based Hybrid System to Cloud Intrusion Detection

Abstract: Cloud is a collection of resources such as hardware, networks, servers, storage, applications, and interfaces to provide on-demand services to customers. Since access to cloud is through internet, data stored in clouds are vulnerable to attacks from external as well as internal intruders. In order to preserve privacy of the data in cloud, several intrusion detection approaches, authentication techniques, and access control policies are being used. The common intrusion detection systems are predominantly incompetent to be used in cloud environments. In this paper, the usage of type-2 fuzzy neural network based on genetic algorithm is discussed to incorporate intrusion detection techniques into cloud. These systems are intelligent to gain knowledge of fuzzy sets and fuzzy rules from data to detect intrusions in a cloud environment. Using a standard benchmark data from a cloud intrusion detection dataset experiments are done, tested, and compared with other existing approaches in terms of detection rate accuracy, precision, recall, MSE, and scalability.

A MAGDM Method Considering the Amount and Reliability Information of Interval-Valued Intuitionistic Fuzzy Sets

Abstract: Intuitionistic fuzzy sets (IFSs) and interval-valued intuitionistic fuzzy sets (IVIFSs) are flexible to deal with the vague and/or imprecise information. Thus, many multi-attribute group decision making (MAGDM) problems are modeled by IFSs or IVIFSs. The comparison of two IVIFSs is still a hot topic, and thereby this paper proposes a new ranking function of IVIFSs, which takes into the amount and the reliability information of an IVIFS and combines the advantages of TOPSIS. Based on the new ranking function, we establish an optimization model to determine the attribute weights when they are unknown and partially known. Moreover, we develop an effective method for solving MAGDM problems in which the attribute values are expressed with IVIFSs. A numerical example of supplier selection problem is examined to demonstrate applicability and feasibility of the proposed method.

A New Interval-valued 2-Tuple Linguistic Bonferroni Mean Operator and Its Application to Multiattribute Group Decision Making

Abstract: The purpose of this paper is to introduce some new Bonferroni mean operators under interval-valued 2-tuple linguistic environment. First, a class of new operational laws of interval-valued 2-tuple linguistic are proposed. Then, we put forward some new interval-valued 2-tuple linguistic Bonferroni mean (IV2TLBM) operators. Moreover, properties and special cases of new aggregation operators are investigated. The main characteristic of the IV2TLBM is that the interrelationship among the input arguments and the closed operations are taken into account. Finally, an approach to multiple attributes group decision making is presented, and a numerical example is given to illustrate the proposed method.

Distributed Consensus Formation Control with Collision and Obstacle Avoidance for Uncertain Networked Omnidirectional Multi-robot Systems Using Fuzzy Wavelet Neural Networks

Abstract: This paper presents a distributed consensus formation control with collision and obstacle avoidance using fuzzy wavelet neural networks (FWNNs) for a group of networked mobile Mecanum-wheeled omnidirectional robots (MWORs) with uncertainties. The dynamic behavior of each uncertain MWOR is modeled by a reduced three-input–three-output second-order state equation with uncertainties, and the multi-MWOR system is modeled by graph theory. Using the Lyapunov stability theory and online learning the system uncertainties via FWNNs, an adaptive and distributed consensus backstepping control approach is presented to carry out formation control in the presence of uncertainties. Collision and obstacle-avoidance methods are provided to avoid any collisions among MWORs and their working environments. Five simulations are conducted to show the effectiveness and merit of the proposed method .

Emotional Fuzzy Sliding-Mode Control for Unknown Nonlinear Systems

Abstract: The brain emotional learning model can be implemented with a simple hardware and processor; however, the learning model cannot model the qualitative aspects of human knowledge. To solve this problem, a fuzzy-based emotional learning model (FELM) with structure and parameter learning is proposed. The membership functions and fuzzy rules can be learned through the derived learning scheme. Further, an emotional fuzzy sliding-mode control (EFSMC) system, which does not need the plant model, is proposed for unknown nonlinear systems. The EFSMC system is applied to an inverted pendulum and a chaotic synchronization. The simulation results with the use of EFSMC system demonstrate the feasibility of FELM learning procedure. The main contributions of this paper are (1) the FELM varies its structure dynamically with a simple computation; (2) the parameter learning imitates the role of emotions in mammalians brain; (3) by combining the advantage of nonsingular terminal sliding-mode control, the EFSMC system provides very high precision and finite-time control performance; (4) the system analysis is given in the sense of the gradient descent method.

Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System

Abstract: A digital signal processor (DSP)-based intelligent sliding-mode control (SMC) is proposed for the position control of a six-phase permanent magnet synchronous motor (PMSM) drive system installed in an electric power steering (EPS) system in this study. First, the dynamic mathematical model of the EPS system is derived by the Lagrangian dynamics. Since the EPS system is a nonlinear and time-varying system, the control accuracy is very sensitive to the parameter variations and external disturbances. Therefore, a SMC is developed for the position control of the EPS system. However, the upper bound of the uncertainties is difficult to obtain in advance and the choice of switching control gain in SMC is vital but time-consuming and may cause undesired chattering phenomenon. Hence, an intelligent SMC with a novel recurrent wavelet fuzzy neural network (ISMC-RWFNN) is proposed, in which a recurrent wavelet fuzzy neural network (RWFNN) is adopted as an uncertainty estimator to overcome the aforementioned disadvantage of SMC. Moreover, a robust compensator is employed to reduce the estimation error. In addition, the adaptive learning algorithms for the online training of the RWFNN are derived using the Lyapunov theorem and Taylor series. Finally, the proposed ISMC-RWFNN to control the position of a six-phase PMSM drive system for the EPS system is implemented in a 32-bit floating-point DSP, and some experimental results are provided to verify its effectiveness.

A Fuzzy Cognitive Map Approach Applied in Cost–Benefit Analysis for Highway Projects

Abstract: [Bagdath, Muhammed Emin Cihangir] Sakarya Univ, Engn Fac, Dept Civil Engn, Sakarya, Turkey. [Akbiyikli, Rifat] Duzce Univ, Technol Fac, Dept Civil Engn, Duzce, Turkey. [Papageorgiou, Elpiniki I.] Hasselt Univ, Fac Business Econ, Hasselt, Belgium. [Papageorgiou, Elpiniki I.] Technol Educ Inst TEI Cent Greece, 3rd Km Old Natl Rd Lamia Athens, Lamia 35100, Greece.

A Fuzzy System for Combining Filter Features Selection Methods

Abstract: Feature selection is considered as one of the most important data pre-processing step in different modelling fields, especially for prediction and classification purposes. Feature selection belongs to the wider class of data mining procedures, as it allows to discover the variables that mostly affect a given phenomenon from an analysis of the available data, by thus increasing the knowledge of the considered process or phenomenon. There are three main categories of feature selection approaches, namely filter, wrappers and embedded methods: this work is focused on the first one and, in particular, on a fuzzy logic-based procedure which combines some traditional filter methods. Filter methods exploit intrinsic properties of the data to select the features before the learning task and, with respect to the other kinds of approaches, require a shorter computational time and adequate for datasets with a large number of instances and features. In order to prove the effectiveness of the proposed approach, several tests have been performed. Different classifiers have been designed and applied for binary classification on different datasets: some widely used public datasets including a lot of instances and features and two datasets coming from the metal industry. The obtained results are presented and discussed in the paper.

BRAIN-F: Beacon Rate Adaption Based on Fuzzy Logic in Vehicular Ad Hoc Network

Abstract: Beacon rate adaption is a way to cope with congestion of the wireless link and it consequently decreases the beacon drop rate and the inaccuracy of information of each vehicle in the network. In a vehicular environment, the beacon rate adjustment is strongly dependent on the traffic condition. Due to this, we firstly propose a new model to detect traffic density based on the vehicle’s own status and the surrounding vehicle’s status. We also develop a model based on fuzzy logic namely the BRAIN-F, to adjust the frequency of beaconing. This model depends on three parameters including traffic density, vehicle status and location status. Channel congestion and information accuracy are considered the main criteria to evaluate the performance of BRAIN-F under both LOS and NLOS. Simulation results demonstrate that the BRAIN-F not only reduces the congestion of the wireless link but it also increases the information accuracy.

Relaxed Controller Design Conditions for Takagi–Sugeno Systems with State Time-Varying Delays

Abstract: This paper deals with the design of fuzzy controllers for Takagi–Sugeno (T-S) fuzzy models with state time-varying delays. New relaxed delay-dependent conditions for the stabilization purpose are proposed in terms of linear matrix inequalities (LMIs), including the knowledge of the bounds of the time-varying delay and its rate of variation. The conservatism improvement is brought through three points: (1) the choice of a convenient augmented Lyapunov–Krasovskii functional candidate, (2) the application of an extension of the Jensen’s inequality, and (3) the Finsler’s lemma. In this context, a parallel distributed compensation control law, which includes both memoryless and delayed state feedbacks, is considered. To apply such control law, it is required to assume that the time-varying delay is available online. Under this assumption, it is highlighted that the proposed LMI-based conditions are significantly relaxed for high rate of variation of the time delay. On the other hand, when this assumption cannot be guaranteed, straightforward corollaries are proposed. A numerical example is provided to illustrate the effectiveness of the proposed LMI-based conditions and their conservatism improvement regarding to previous results.