International Journal of Fuzzy Systems (INT J FUZZY SYST)

Current impact factor: 1.10

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 1.095
2013 Impact Factor 1.031
2012 Impact Factor 1.506
2011 Impact Factor 1.157
2010 Impact Factor 1.362
2009 Impact Factor 1.09

Impact factor over time

Impact factor
Year

Additional details

5-year impact 1.37
Cited half-life 4.00
Immediacy index 0.15
Eigenfactor 0.00
Article influence 0.16
ISSN 1562-2479

Publications in this journal


  • No preview · Article · Feb 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: The objective of this paper is to propose a generalized approach to state-feedback stabilization of interconnected fuzzy systems. The proposed algorithms, which are formulated within the convex optimization framework, provide decentralized solutions to the problem of asymptotic stability with strict dissipativity. It is established that the new methodology can reproduce earlier results on passivity, positive realness, and disturbance attenuation. Numerical examples are presented to illustrate the applicability of the design method.
    No preview · Article · Feb 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: This paper presents a new approach for projecting a fuzzy number over a crisp closed convex set. Based on this approach, a kind of fuzzy linear projection equation is introduced and also it is used to solve a fuzzy system of linear equations with crisp variables, fuzzy right-hand side, and fuzzy coefficients. The proposed definition for fuzzy projection is based on \(\alpha-\)cut approach. Numerical examples illustrate the applicability of new approach for solving fuzzy system of linear equations with crisp variables. However, the applications of fuzzy projection cannot be limited just to solving a fuzzy system of linear equations.
    No preview · Article · Feb 2016 · International Journal of Fuzzy Systems
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    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.
    No preview · Article · Feb 2016 · International Journal of Fuzzy Systems
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    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.
    No preview · Article · Feb 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: This paper describes parametric analysis in fuzzy number linear programming (FNLP) when the objective function coefficients and/or the right-hand-side constants are parameterized. Using a linear ranking function, we consider the problem variations. In fact, we find a range set of the parameters for which a given basis remains optimal for the FNLP problem. If the perturbation destroys optimality and/or feasibility of the optimal basis, we use of the fuzzy primal simplex method, the fuzzy dual simplex method and/or our proposed fuzzy primal-dual simplex method to find the new optimal basis. Finally, by numerical examples we demonstrate the computational procedure.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: This article is concerned with the design of polynomial fuzzy controller for a class of polynomial fuzzy model subject to both state delay and actuator saturation. Based on polytopic model of the input saturation, two design methods are proposed. In the first method, a specific Lyapunov Krasovskii function is proposed to transform the nonconvex sum of squares (SOS) conditions into convex SOS ones. The second method overcomes the restriction of the first method by bounding the state derivative. The obtained results are formulated in terms of SOS matrices which can be symbolically and numerically solved via the SOSTOOLS and the SeDuMi. Moreover, an attractive region of initial states that ensures asymptotic stability of polynomial fuzzy model is determined. Two numerical examples are given to show the effectiveness of the proposed methods.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    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.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: A novel proportional-integral-derivative-based fuzzy neural network (PID-based FNN) controller is proposed in this study to control the speed of a vane-type air motor (VAM) servo system for tracking periodic speed command. First, the structure and operating principles of the VAM servo system are introduced. Then, the dynamics of the VAM servo system is analyzed to derive the second-order state equation of the VAM. Moreover, due to the dynamic characteristics and system parameters of the VAM servo system are highly nonlinear and time-varying, a PID-based FNN controller, which integrates conventional proportional-integral-derivative neural network (PIDNN) control with fuzzy rules, is proposed to achieve precise speed control of VAM servo system under the occurrences of the inherent nonlinearities and external disturbances. The network structure and its on-line learning algorithm using delta adaptation law are described in detail. Meanwhile, the convergence analysis of the speed tracking error is given using the discrete-type Lyapunov function. To enhance the control performance of the proposed intelligent control approach, a 32-bit floating-point digital signal processor (DSP), TMS320F28335, is adopted for the implementation of the proposed control system. Finally, experimental results are illustrated to show the validity and advantages of the proposed PID-based FNN controller for VAM servo system.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: Wheelchair users often struggle to drive safely and accompanied by caregivers. Wheelchair robots can employ the autonomous functions to avoid obstacles and reduce the workload of the caregivers. To achieve the goal, the accompanist needs to be steadily recognized and tracked by the robots. By using Q-learning based accompanist tracking fuzzy controller, wheelchair robots can peacefully follow the accompanist. Meanwhile, it is important to make the people on wheelchair robots feel comfortable. Based on ISO 2631-1, the ride qualities are obtained by averaging the acceleration data in the frequency bands, and the critical thresholds utilizing to assess the ride comfort are also determined inside. To make the level of bumpiness be as multiple as possible, four kinds of pavements are chosen in the experiments. The results show that the feelings of the occupants on the wheelchair robot are quite different from the ride comfort defined in ISO 2631-1, so a new standard is proposed to assess the ride comfort in this study. The accuracy of the proposed standard is 90.67 %, which is higher than that of ISO 2631-1, 42.48 %. Furthermore, to the best of our knowledge, this paper is thought to be the first one to present the ISO 2631-1-based comfort criterion for wheelchair robots.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: In Taiwan, more and more enterprises face the problems of financial distress in recent years. Besides, it is also noted that the volume of outstanding debt to corporations increases in Taiwan. An improvement in distress prediction accuracy can lead to save tens of billions of dollars. The firms which face financial distress will reveal many signs on financial data. Therefore, this study hopes to provide to managers and investors as a reference for decisions making through systematic approach as it can look for firms facing financial distress. In this paper, a novel prediction system is proposed which is based on intelligent classification to distinguish bankruptcy prediction. This method is referred to as a cerebellar model neural network (CMNN). A CMNN can be thought of as a learning mechanism imitating the cerebellum of a human being. Through training, this CMNN can be viewed like an expert of financial analyzer and then it can be applied to bankruptcy prediction. This study uses an artificial neural network, a genetic programming, and the proposed CMNN to construct financial distress prediction models and compare the performance of above three models using some Taiwanese company data, and it confirms CMNN is better than the others. By doing this, it can help understand and predict financial condition of firms and prevent the firms from insolvency. The CMNN yields the best prediction through the efficient infer reasoning of CMNN. Thus, the result is feasible to construct the financial distress prediction model.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: This paper presents a backstepping holonomic tracking control method of embedded wheeled robots using an evolutionary fuzzy system with qualified ant colony optimization (ACO). The Taguchi method is applied to design an optimal ACO via an orthogonal array. This qualified ACO is then employed to develop an evolutionary fuzzy system, called FS-TACO. The proposed hybrid intelligent fuzzy system FS-TACO is realized in a field-programmable gate array (FPGA) to dynamic tracking control of three-wheeled holonomic mobile robots. In comparison with conventional control systems, this approach takes the advantages of Taguchi quality method, fuzzy system, ACO and FPGA technique, thereby obtaining better population diversity, avoiding premature convergence, and achieving self-adaptive holonomic control. The FPGA realization using system-on-a-programmable chip methodology of the proposed FS-TACO is more effective in practice for real-world embedded applications. Experimental results and comparative works are conducted to exhibit the merits of the proposed FPGA-based FS-TACO controller for three-wheeled holonomic mobile robots.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: This paper revisits the problem of \(H_{\infty }\) filtering design for nonlinear systems with time-varying delay through Takagi–Sugeno (T–S) fuzzy models. By applying the fuzzy line-integral Lyapunov function, a new sufficient condition for the existence of the fuzzy \(H_{\infty }\) filter design is established in terms of linear matrix inequalities. The present method provides improvements and produces better results than existing ones in the literature. Two examples are given to show the advantages and effectiveness of the proposed results.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: This paper is concerned with networked \(L_2\)-gain control for a T-S fuzzy system over an event-triggered communication network. Taking an event-triggered communication scheme and network-induced delays into consideration, the networked control system is described by an asynchronous T-S fuzzy system with an interval time-varying delay. Due to variation characteristic of network-induced delays, the interval is decomposed into N subintervals and the jumping among these subintervals is governed by a Markov chain. A new relaxation method, which fully utilizes the convexity of normalized membership functions and the deviation bounds of asynchronous normalized membership functions, is proposed and a stochastic Lyapunov–Krasovskii functional is constructed to derive some delay-dependent criteria on \(L_2\)-gain performance analysis and controller design of the asynchronous T-S fuzzy system. An illustrative example is provided to show that the proposed criteria are of less conservatism and less computational complexity than some existing results, and are effective in achieving a prescribed \(L_2\)-gain performance.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: In this paper, we develop the scaled prioritized intuitionistic fuzzy interaction averaging operator, the scaled prioritized intuitionistic fuzzy weighted interaction averaging operator, the generalized scaled prioritized intuitionistic fuzzy interaction averaging operator, and the generalized scaled prioritized intuitionistic fuzzy weighted interaction averaging operator. The main advantages of these new operators are that the prioritized relationships between criteria are evaluated by priority labels in known situations and unknown situations, and the interactions between membership function and non-membership function of different intuitionistic fuzzy numbers are considered. Moreover, some properties of these new information aggregation operators are investigated, an approach to multiple attributes group decision making is given and an example is illustrated to show the validity and feasibility of the new approach.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    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.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems
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    ABSTRACT: This article describes a data fusion algorithm that uses fuzzy logic techniques. The algorithm involves weighting factors, the values of which change depending on whether the conditions dictated by processes of fuzzy logic have been met. By modifying the values of weighting factors, one can achieve a measurement signal with expected properties. The article includes a general description of the algorithm and an example of its application. The algorithm was developed for the purposes of data fusion for contactless measurement of the linear and angular position of an automatic laparoscopic device or a laparoscopic camera (hereinafter Laparoscope sleeve or LS). These measurements are used in the article to close the feedback loop on the position within a servomechanism with two degrees of freedom.
    No preview · Article · Jan 2016 · International Journal of Fuzzy Systems