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Fuzzy Systems, IEEE Transactions on

Issue 1 • Date Feb. 2008

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Displaying Results 1 - 25 of 28
  • Table of contents

    Publication Year: 2008 , Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Fuzzy Systems publication information

    Publication Year: 2008 , Page(s): C2
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  • Aggregation Using the Fuzzy Weighted Average as Computed by the Karnik–Mendel Algorithms

    Publication Year: 2008 , Page(s): 1 - 12
    Cited by:  Papers (43)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (516 KB) |  | HTML iconHTML  

    By connecting work from two different problems-the fuzzy weighted average (FWA) and the generalized centroid of an interval type-2 fuzzy set-a new alpha-cut algorithm for solving the FWA problem has been obtained, one that is monotonically and superexponentially convergent. This new algorithm uses the Karnik-Mendel (KM) algorithms to compute the FWA -cut end-points. It appears that the KM -cut algorithms approach for computing the FWA requires the fewest iterations to date, and may therefore be the fastest available FWA algorithm to date. View full abstract»

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  • Fuzzy Interpolation and Extrapolation: A Practical Approach

    Publication Year: 2008 , Page(s): 13 - 28
    Cited by:  Papers (49)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (900 KB) |  | HTML iconHTML  

    Fuzzy interpolation does not only help to reduce the complexity of fuzzy models, but also makes inference in sparse rule-based systems possible. It has been successfully applied to systems control, but limited work exists for its applications to tasks like prediction and classification. Almost all fuzzy interpolation techniques in the literature make strong assumptions that there are two closest adjacent rules available to the observation, and that such rules must flank the observation for each attribute. Also, some interpolation approaches cannot handle fuzzy sets whose membership functions involve vertical slopes. To avoid such limitations and develop a more practical approach, this paper extends the work of Huang and Shen. The result enables both interpolation and extrapolation which involve multiple fuzzy rules, with each rule consisting of multiple antecedents. Two realistic applications, namely truck backer-upper control and computer activity prediction, are provided in this paper to demonstrate the utility of the extended approach. Experiment-based comparisons to the most commonly used Mamdani fuzzy reasoning mechanism, and to other existing fuzzy interpolation techniques are given to show the significance and potential of this research. View full abstract»

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  • Applying 2-Tuple Multigranularity Linguistic Variables to Determine the Supply Performance in Dynamic Environment Based on Product-Oriented Strategy

    Publication Year: 2008 , Page(s): 29 - 39
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (962 KB) |  | HTML iconHTML  

    Supply performance assessment is not only the core mechanism for supplier management, but also the major task for enterprises in implementing supply chain management. Consequently, supply chain system implementation should rely on the results of supply performance assessments to supervise suppliers. However, supply performance assessment is always concerned with numerous supply behaviors, and the information obtained from these supply behaviors often varies during the measurement timeframe. Because of uncertainty and inaccuracy, the numerical data are not appropriate for demonstrating dynamic information. This study employs 2-tuple multigranularity linguistic variables to deal with dynamic information appropriately and assess supply performance, and even permits decision makers to introduce different linguistic terms (linguistic scale) based on consideration of supply behavior characteristics. The 2-tuple multigranularity linguistic variables present the information via linguistic and numeric results, both where the linguistic result could express the variation of dynamic information appropriately, and where the numerical result could further reflect the difference based on the same linguistic result to improve the sensitivity. Additionally, this study also employs a fuzzy linguistic quantifier to simulate the decision criteria based on the product strategy to further obtain the modified linguistic ordered weighted averaging operator with maximum entropy that will be exercised to aggregate the 2-tuple linguistic information. This investigation provides decision makers with rapid access to complete, and integrated information on the practical supply performance of suppliers as appropriate. View full abstract»

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  • A Combined Adaptive Law for Fuzzy Iterative Learning Control of Nonlinear Systems With Varying Control Tasks

    Publication Year: 2008 , Page(s): 40 - 51
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (431 KB) |  | HTML iconHTML  

    To deal with the iterative control of uncertain nonlinear systems with varying control tasks, nonzero initial resetting state errors, and nonrepeatable mismatched input disturbance, a new adaptive fuzzy iterative learning controller is proposed in this paper. The main structure of this learning controller is constructed by a fuzzy learning component and a robust learning component. For the fuzzy learning component, a fuzzy system used as an approximator is designed to compensate for the plant nonlinearity. For the robust learning component, a sliding-mode-like strategy is applied to overcome the nonlinear input gain, input disturbance, and fuzzy approximation error. Both designs are based on a time-varying boundary layer which is introduced not only to solve the problem of initial state errors but also to eliminate the possible undesirable chattering behavior. A new adaptive law combining time- and iteration-domain adaptation is derived to search for suitable values of control parameters and then guarantee the closed-loop stability and error convergence. This adaptive algorithm is designed without using projection or deadzone mechanism. With a suitable choice of the weighting gain, the memory size for the storage of parameter profiles can be greatly reduced. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. Moreover, the norm of tracking state error vector will asymptotically converge to a tunable residual set even when the desired tracking trajectory is varying between successive iterations. View full abstract»

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  • Analytical Structures for Fuzzy PID Controllers?

    Publication Year: 2008 , Page(s): 52 - 60
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1011 KB) |  | HTML iconHTML  

    In this paper, analytical structures for fuzzy proportional-integral-derivative (PID) controllers are derived by using triangular membership functions for inputs, singletons, or triangular membership functions for output, minimum triangular norm, maximum or drastic sum triangular conorm, Mamdani minimum, drastic or Larsen product inference, nonlinear control rules, and center-of-sum defuzzification. It is shown that these analytical structures are not suitable for control purpose. In this context, it is extremely important to note that the analytical structures reported by Carvajal et al. are also not valid for control. View full abstract»

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  • Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps

    Publication Year: 2008 , Page(s): 61 - 72
    Cited by:  Papers (32)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (856 KB) |  | HTML iconHTML  

    In this paper, we introduce a novel approach to time-series prediction realized both at the linguistic and numerical level. It exploits fuzzy cognitive maps (FCMs) along with a recently proposed learning method that takes advantage of real-coded genetic algorithms. FCMs are used for modeling and qualitative analysis of dynamic systems. Within the framework of FCMs, the systems are described by means of concepts and their mutual relationships. The proposed prediction method combines FCMs with granular, fuzzy-set-based model of inputs. One of their main advantages is an ability to carry out modeling and prediction at both numerical and linguistic levels. A comprehensive set of experiments has been carried out with two major goals in mind. One is to assess quality of the proposed architecture, the other to examine the influence of its parameters of the prediction technique on the quality of prediction. The obtained results, which are compared with other prediction techniques using fuzzy sets, demonstrate that the proposed architecture offers substantial accuracy expressed at both linguistic and numerical levels. View full abstract»

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  • An Efficient Procedure for Solving a Fuzzy Relational Equation With Max–Archimedean t-Norm Composition

    Publication Year: 2008 , Page(s): 73 - 84
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB) |  | HTML iconHTML  

    In the literature, a necessary condition for minimal solutions of a fuzzy relational equation with max-product composition shows that each of its components is either zero or the corresponding component's value of the greatest solution. In this paper, we first extend this necessary condition to the situation with max-Archimedean triangular-norm (t-norm) composition. Based on this necessary condition, we then propose rules to reduce the problem size so that the complete set of minimal solutions can be computed efficiently. Furthermore, rather than work with the actual equations, we employ a simple matrix whose elements capture all of the properties of the equations in finding the minimal solutions. Numerical examples with specific cases of the max-Archimedean t-norm composition are provided to illustrate the procedure. View full abstract»

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  • Hybrid Fuzzy Model-Based Control of Nonholonomic Systems: A Unified Viewpoint

    Publication Year: 2008 , Page(s): 85 - 96
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (661 KB) |  | HTML iconHTML  

    This paper proposes a unified hybrid fuzzy model-based control scheme for uncertain nonholonomic systems. Compared with typical hybrid fuzzy control, the stability analysis is performed based on a new concept of constructing a semicommon Lyapunov function and a new definition called as exponential-like model following. This advancement provides a strict stability analysis but results in relaxed gain conditions. In detail, a unified hybrid Takagi-Sugeno fuzzy model is first introduced for representing well-known nonholonomic systems with a momentum conservation constraint or a no-slip constraint. Then, the hybrid fuzzy controller is derived to ensure robust nonlinear model following control, i.e., an asymptotic convergence with adjustable ultimate bound and arbitrary disturbance attenuation in an -gain sense. Furthermore, an iterative linear matrix inequality technique is proposed to guarantee the stability and avoid the need of a common positive-definite matrix. Finally, the applications are carried out on a hopping robot and a car-like mobile robot. Numerical simulations and experiment results show the expected performances. View full abstract»

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  • Fuzzy Decentralized Sliding-Mode Control of a Car-Like Mobile Robot in Distributed Sensor-Network Spaces

    Publication Year: 2008 , Page(s): 97 - 109
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1506 KB) |  | HTML iconHTML  

    In this paper, the trajectory tracking and (dynamic) obstacle avoidance of a car-like mobile robot (CLMR) within distributed sensor-network spaces via fuzzy decentralized sliding-mode control (FDSMC) is developed. To implement trajectory tracking and (dynamic) obstacle avoidance, two distributed charge-coupled device (CCD) cameras are set up to realize the dynamic position of the CLMR and the obstacle. Based on the control authority of these two CCD cameras, a suitable reference trajectory including desired steering angle and forward-backward velocity for the proposed controller of the CLMR is planned. It is also transmitted to the CLMR by a wireless module. The proposed FDSMC can track a reference trajectory without the requirement of a mathematical model. Only the input-output data pairs of the CLMR and the upper bound of its dynamics are required for the selection of suitable scaling factors. The proposed control system includes two processors with multiple sampling rates. One is a personal computer employed to obtain the image of the CLMR and the obstacle, to plan a reference trajectory for the CLMR, and then to transmit the planned reference trajectory to the CLMR. The other is a digital signal processor (DSP) implementing in the CLMR to control two dc motors. Finally, a sequence of experiments is carried out to confirm the performance of the proposed control system. View full abstract»

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  • An Intelligent System for Machinery Condition Monitoring

    Publication Year: 2008 , Page(s): 110 - 122
    Cited by:  Papers (15)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (764 KB) |  | HTML iconHTML  

    A reliable monitoring system is critically needed in a wide range of industries to detect the occurrence of a fault to prevent machinery performance degradation, malfunction, and sudden failure. In this paper, a new intelligent system, extended neurofuzzy (ENF) scheme, is proposed for real-time machinery condition monitoring. The monitoring reliability is improved by integrating the predicted machinery condition to fault diagnosis. The ENF scheme can perform both classification and prediction operations. The ENF classifier integrates the merits of several signal processing techniques for a more positive assessment of the machinery condition. The ENF predictor forecasts the machinery condition propagation trends. An interscheme training technique is proposed to improve the ENF system's adaptive capability to accommodate different operation conditions. The viability of this new monitoring system has been verified by experimental tests. Test results have shown that the developed ENF system is a robust condition monitoring tool that has good adaptive capabilities to accommodate different machinery conditions. View full abstract»

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  • Entropy of Credibility Distributions for Fuzzy Variables

    Publication Year: 2008 , Page(s): 123 - 129
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (302 KB) |  | HTML iconHTML  

    This paper deals with the degree of uncertainty associated with fuzzy variables. Based on the notion of credibility measure, a definition of entropy is formulated from an information theoretical view and its properties are investigated. Finally, some comments are given on the construction of alternative definitions of entropy and axiomatic characterization. View full abstract»

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  • On the Law of Importation (x \wedge y) \longrightarrow z \equiv (x \longrightarrow (y \longrightarrow z)) in Fuzzy Logic

    Publication Year: 2008 , Page(s): 130 - 144
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (497 KB) |  | HTML iconHTML  

    The law of importation, given by the equivalence (x Lambda y) rarr z equiv (xrarr (y rarr z)), is a tautology in classical logic. In A-implications defined by Turksen et aL, the above equivalence is taken as an axiom. In this paper, we investigate the general form of the law of importation J(T(x, y), z) = J(x, J(y, z)), where T is a t-norm and J is a fuzzy implication, for the three main classes of fuzzy implications, i.e., R-, S- and QL-implications and also for the recently proposed Yager's classes of fuzzy implications, i.e., f- and g-implications. We give necessary and sufficient conditions under which the law of importation holds for R-, S-, f- and g-implications. In the case of QL-implications, we investigate some specific families of QL-implications. Also, we investigate the general form of the law of importation in the more general setting of uninorms and t-operators for the above classes of fuzzy implications. Following this, we propose a novel modified scheme of compositional rule of inference (CRI) inferencing called the hierarchical CRI, which has some advantages over the classical CRI. Following this, we give some sufficient conditions on the operators employed under which the inference obtained from the classical CRI and the hierarchical CRI become identical, highlighting the significant role played by the law of importation. View full abstract»

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  • A New Fuzzy Set Merging Technique Using Inclusion-Based Fuzzy Clustering

    Publication Year: 2008 , Page(s): 145 - 161
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1612 KB) |  | HTML iconHTML  

    This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets. View full abstract»

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  • On the Stability of Takagi–Sugeno Fuzzy Systems With Time-Varying Uncertainties

    Publication Year: 2008 , Page(s): 162 - 170
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (385 KB) |  | HTML iconHTML  

    This paper studies the problems of stability analysis of Takagi-Sugeno free fuzzy systems with time-varying uncertainties. In our prior study, we represented the time-varying uncertainty incurred in characteristic interval matrices in terms of the stability of Takagi-Sugeno free fuzzy systems with consequent parameter uncertainties. Based on Mayer's convergent theorem for powers of single interval matrix and its generalization, we further proposed some sufficient conditions for the Takagi-Sugeno free fuzzy system with time-varying uncertainties to be globally asymptotically stable. In this paper, we propose the notion of simultaneously nilpotent interval matrices to investigate the Takagi-Sugeno free fuzzy system with time-varying uncertainties to be strongly stable within steps, where relates to the dimension of interval matrices. Moreover, a unique situation for the deterministic Takagi-Sugeno free fuzzy system to be strongly stable within steps is derived as well, where relates to the dimension of characteristic matrices for the deterministic Takagi-Sugeno free fuzzy system. View full abstract»

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  • A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators

    Publication Year: 2008 , Page(s): 171 - 186
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1466 KB) |  | HTML iconHTML  

    This paper develops an adaptive fuzzy controller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein the reinforcement signal is used to tune online the conclusion part of a fuzzy Markov game controller. The proposed Markov game-adaptive fuzzy controller uses a simple fuzzy inference system (FIS), is computationally efficient, generates a swift control, and requires no exact dynamics of the robot system. To illustrate the superiority of Markov game-adaptive fuzzy control, we compare the performance of the controller against a) the Markov game-based robust neural controller, b) the reinforcement learning (RL)-adaptive fuzzy controller, c) the FQL controller, d) the Hinfin theory-based robust neural game controller, and e) a standard RL-based robust neural controller, on two highly nonlinear robot arm control problems of i) a standard two-link rigid robot arm and ii) a 2-DOF SCARA robot manipulator. The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories. The results also demonstrate the viability of FISs for accelerating learning in Markov games and extending Markov game-based control to continuous state-action space problems. View full abstract»

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  • PI Adaptive Fuzzy Control With Large and Fast Disturbance Rejection for a Class of Uncertain Nonlinear Systems

    Publication Year: 2008 , Page(s): 187 - 197
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (417 KB) |  | HTML iconHTML  

    Design of controllers for uncertain systems is inherently paradoxical. Adaptive control approaches claim to adapt system parameters against uncertainties, but only if these uncertainties change slowly enough. Alternatively, robust control methodologies claim to ensure system stability against uncertainties, but only if these uncertainties remain within known bounds. This is while, in reality, disturbances and uncertainties remain faithfully uncertain, i.e., may be both fast and large. In this paper, a PI-adaptive fuzzy control architecture for a class of uncertain nonlinear systems is proposed that aims to provide added robustness in the presence of large and fast but bounded uncertainties and disturbances. While the proposed approach requires the uncertainties to be bounded, it does not require this bound to be known. Lyapunov analysis is used to prove asymptotic stability of the proposed approach. Application of the proposed method to a second-order inverted pendulum system demonstrates the effectiveness of the proposed approach. Specifically, system responses to fast versus slow and large versus small disturbances are considered in the presented simulation studies. View full abstract»

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  • A Type-2 Fuzzy Approach to Linguistic Summarization of Data

    Publication Year: 2008 , Page(s): 198 - 212
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (995 KB) |  | HTML iconHTML  

    This paper introduces an application of type-2 fuzzy sets in data linguistic summarization. The original approach by Yager (1982) based on representing natural language statements via type-1, i.e., the Zadeh fuzzy sets, is generalized with type-2 fuzzy sets applied as models of linguistically expressed quantities and/or properties of objects. Type-2 sets extend the known summarization procedures by handling fuzzy values stored in databases, and allow to represent a linguistic term via a few different membership functions (e.g., provided by different experts), which makes the method more general and human-consistent. Furthermore, quality measures for type-2 summaries are discussed in order to evaluate the informativeness of the messages generated. Finally, two prototype applications are presented and the success of the new method is discussed. View full abstract»

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  • Parameter Identification of Recurrent Fuzzy Systems With Fuzzy Finite-State Automata Representation

    Publication Year: 2008 , Page(s): 213 - 224
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (992 KB) |  | HTML iconHTML  

    This paper presents the identification of nonlinear dynamical systems by recurrent fuzzy system (RFS) models. Two types of RFS models are discussed: the Takagi-Sugeno-Kang (TSK) type and the linguistic or Mamdani type. Both models are equivalent and the latter model may be represented by a fuzzy finite-state automaton (FFA). An identification procedure is proposed based on a standard general purpose genetic algorithm (GA). First, the TSK rule parameters are estimated and, in a second step, the TSK model is converted into an equivalent linguistic model. The parameter identification is evaluated in some benchmark problems for nonlinear system identification described in literature. The results show that RFS models achieve good numerical performance while keeping the interpretability of the actual system dynamics. View full abstract»

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  • Minimum Classification Error Training for Choquet Integrals With Applications to Landmine Detection

    Publication Year: 2008 , Page(s): 225 - 238
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (774 KB) |  | HTML iconHTML  

    A novel algorithm for discriminative training of Choquet-integral-based fusion operators is described. Fusion is performed by Choquet integration of classifier outputs with respect to fuzzy measures. The fusion operators are determined by the parameters of fuzzy measures. These parameters are found by minimizing a minimum classification error (MCE) objective function. The minimization is performed with respect to a special class of measures, the Sugeno lambda-measures. An analytic expression is derived for the gradient of the Choquet integral with respect to the Sugeno lambda-measure. The new algorithm is applied to a landmine detection problem, and compared to previous techniques. View full abstract»

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  • A Fuzzy Probabilistic Approach for Determining Safety Integrity Level

    Publication Year: 2008 , Page(s): 239 - 248
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (637 KB) |  | HTML iconHTML  

    The process industry has always been faced with the difficult task of determining the required integrity of safeguarding systems such as safety instrumented systems (SISs). The ANSI/ISA S84.01-1996 and IEC 61508 safety standards provide guidelines for the design, installation, operation, maintenance, and test of SIS. However, in the field, there is a considerable lack of understanding of how to apply these standards to both determine and achieve the required safety integrity level (SIL) for SIS. Moreover, in certain situations, the SIL evaluation is further complicated due to the uncertainty on reliability parameters of SIS components. This paper proposes a new approach to evaluate the ldquoconfidencerdquo of the SIL determination when there is an uncertainty about failure rates of SIS components. This approach is based on the use of failure rates and fuzzy probabilities to evaluate the SIS failure probability on demand and the SIL of the SIS. Furthermore, we provide guidance on reducing the SIL uncertainty based on fuzzy probabilistic importance measures. View full abstract»

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  • Cluster-Based Evaluation in Fuzzy-Genetic Data Mining

    Publication Year: 2008 , Page(s): 249 - 262
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3166 KB) |  | HTML iconHTML  

    Data mining is commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transactions in real-world applications, however, usually consist of quantitative values. In the past, we proposed a fuzzy-genetic data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. It used a combination of large 1-itemsets and membership-function suitability to evaluate the fitness values of chromosomes. The calculation for large 1-itemsets could take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this paper, an enhanced approach, called the cluster-based fuzzy-genetic mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes in a population into clusters by the - means clustering approach and evaluates each individual according to both cluster and their own information. Experimental results also show the effectiveness and efficiency of the proposed approach. View full abstract»

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  • Iterative Fuzzy Clustering Algorithm With Supervision to Construct Probabilistic Fuzzy Rule Base From Numerical Data

    Publication Year: 2008 , Page(s): 263 - 277
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1538 KB) |  | HTML iconHTML  

    To deal with data patterns with linguistic ambiguity and with probabilistic uncertainty in a single framework, we construct an interpretable probabilistic fuzzy rule-based system that requires less human intervention and less prior knowledge than other state of the art methods. Specifically, we present a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustering process. The learning process starts in a fully unsupervised manner using fuzzy c-means (FCM) clustering algorithm and a cluster validity criterion, and then gradually constructs meaningful fuzzy partitions over the input space. The corresponding fuzzy rules with probabilities are obtained through an iterative learning process of selecting clusters with supervisory guidance based on the notions of cluster-pureness and class-separability. The proposed algorithm is tested first with synthetic data sets and benchmark data sets from the UCI Repository of Machine Learning Database and then, with real facial expression data and TV viewing data. View full abstract»

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  • World Congress on Computational Intelligence - WCCI 2008

    Publication Year: 2008 , Page(s): 278
    Save to Project icon | Request Permissions | PDF file iconPDF (627 KB)  
    Freely Available from IEEE

Aims & Scope

The IEEE Transactions on Fuzzy Systems (TFS) is published quarterly. TFS will consider papers that deal with the theory, design or an application of fuzzy systems ranging from hardware to software.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Chin-Teng Lin
National Chiao-Tung University