By Topic

Fuzzy Systems, IEEE Transactions on

Issue 4 • Date Nov. 1998

Filter Results

Displaying Results 1 - 17 of 17
  • Comments on "H/sub /spl infin// tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach" [with reply]

    Page(s): 605 - 606
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (65 KB)  

    The objective of H/sub /spl infin// disturbance attenuation problem is to attenuate the effect of disturbance to the prescribed level and achieve performance robustness. Chen, Lee and Chang (Fuzzy Syst., vol.4, p.32-43, 1996) proposed an adaptive fuzzy H/sub /spl infin// disturbance attenuation algorithm. The fuzzy approximation error, which is influenced by the control input, is taken as the disturbance signal in the proposed algorithm. The authors argue that, because of the tradeoff between the attenuation level and the control input, performance robustness cannot be achieved by the proposed algorithm. The original authors agree that the approximation error will influence the performance robustness, especially in the case without external disturbance and with very small attenuation level. However, in practical control applications, very small attenuation levels are avoided in order to prevent high-gain control. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Author index

    Page(s): 1 - 3
    Save to Project icon | Request Permissions | PDF file iconPDF (165 KB)  
    Freely Available from IEEE
  • Subject index

    Page(s): 3 - 8
    Save to Project icon | Request Permissions | PDF file iconPDF (158 KB)  
    Freely Available from IEEE
  • Robust stability analysis and design method for the fuzzy feedback linearization regulator

    Page(s): 464 - 472
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB)  

    A robust stability analysis and design method for a fuzzy feedback linearization regulator is presented. The well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model. Uncertainties and disturbance are assumed to be included in the model structure with known bounds. For these structured uncertainties, stability robustness of the closed system is analyzed in both input-output sense and Lyapunov sense. The robust stability conditions are proposed using multivariable circle criterion and the relationship between input-output stability and Lyapunov stability. Also, based on the stability analysis, a systematic design procedure for the fuzzy feedback linearization regulator is provided. The effectiveness of the proposed analysis and design method is illustrated by a simple example View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Design of new adaptive fuzzy logic controller for nonlinear plants with unknown or time-varying dead zones

    Page(s): 482 - 491
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (284 KB)  

    An adaptive fuzzy logic controller (FLC) is designed for plants with unknown and/or time-varying dead zones. The steady-state control resolutions with perturbing action, which are different from the ones in the transient states, are used to cancel out the unknown and/or time-varying dead-zone effects. Automatically adjusted control resolutions play a key role as a fuzzy dead-zone inverse. The control resolutions of the control input variables are dependent on the scaling gains of the variables. Therefore, we can develop the fuzzy dead-zone inverse by reperturbing and adjusting the scaling gains adequately in the steady-states. The developed fuzzy logic controllers that are applied to the plants with unknown dead zones ensure their effectiveness even though the dead-zone characteristics are time varying View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • General SISO Takagi-Sugeno fuzzy systems with linear rule consequent are universal approximators

    Page(s): 582 - 587
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB)  

    Takagi-Sugeno (TS) fuzzy systems have been employed as fuzzy controllers and fuzzy models in successfully solving difficult control and modeling problems in practice. Virtually all the TS fuzzy systems use linear rule consequent. At present, there exist no results (qualitative or quantitative) to answer the fundamentally important question that is especially critical to TS fuzzy systems as fuzzy controllers and models, “Are TS fuzzy systems with linear rule consequent universal approximators?” If the answer is yes, then how can they be constructed to achieve prespecified approximation accuracy and what are the sufficient renditions on systems configuration? In this paper, we provide answers to these questions for a general class of single-input single-output (SISO) fuzzy systems that use any type of continuous input fuzzy sets, TS fuzzy rules with linear consequent and a generalized defuzzifier containing the widely used centroid defuzzifier as a special case. We first constructively prove that this general class of SISO TS fuzzy systems can uniformly approximate any polynomial arbitrarily well and then prove, by utilizing the Weierstrass approximation theorem, that the general TS fuzzy systems can uniformly approximate any continuous function with arbitrarily high precision. Furthermore, we have derived a formula as part of sufficient conditions for the fuzzy approximation that can compute the minimal upper bound on the number of input fuzzy sets and rules needed for any given continuous function and prespecified approximation error bound, An illustrative numerical example is provided View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Improving the interpretability of TSK fuzzy models by combining global learning and local learning

    Page(s): 530 - 537
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB)  

    The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is essentially a multimodel approach in which simple submodels (typically linear models) are combined to describe the global behavior of the system. Most existing learning algorithms for identifying the TSK model are based on minimizing the square of the residual between the overall outputs of the real system and the identified model. Although these algorithms can generate a TSK model with good global performance (i.e., the model is capable of approximating the given system with arbitrary accuracy, provided that sufficient rules are used and sufficient training data are available), they cannot guarantee the resulting model to have a good local performance. Often, the submodels in the TSK model may exhibit an erratic local behavior, which is difficult to interpret. Since one of the important motivations of using the TSK model (also other fuzzy models) is to gain insights into the model, it is important to investigate the interpretability issue of the TSK model. We propose a new learning algorithm that integrates global learning and local learning in a single algorithmic framework. This algorithm uses the idea of local weighed regression and local approximation in nonparametric statistics, but remains the component of global fitting in the existing learning algorithms. The algorithm is capable of adjusting its parameters based on the user's preference, generating models with good tradeoff in terms of global fitting and local interpretation. We illustrate the performance of the proposed algorithm using a motorcycle crash modeling example View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Feedback linearization control design for systems with fuzzy uncertainty

    Page(s): 492 - 503
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (592 KB)  

    We derive a state feedback control design for nonlinear systems with fuzzy uncertainty. We use a fuzzy version of the Kolmogorov forward equation and an equivalent deterministic system to represent the effects of the fuzzy uncertainty. Feedback linearization of the equivalent deterministic system cancels both the system nonlinearities and the effects of the fuzzy uncertainty on the state membership function. Our controller obtains asymptotic stability and an approximation to our controller drives the system state to a small bounded set View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fuzzy-based rate control for real-time MPEG video

    Page(s): 504 - 516
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB)  

    We propose a fuzzy logic-based control scheme for real-time motion picture expert group (MPEG) video to avoid long delay or excessive loss at the user-network interface (UNI) in an asynchronous transfer mode (ATM) network. The system consists of a shaper whose role is to smooth the MPEG output traffic to reduce the burstiness of the video stream. The input and output rates of the shaper buffer are controlled by two fuzzy logic-based controllers. To avoid a long delay at the shaper, the first controller aims to tune the output rate of the shaper in the video frame time scale based on the number of available transmission credits at the UNI and the occupancy of the shaper's buffer. Based on the average occupancy of the shaper's buffer and its variance, the second controller tunes the input rate to the shaper over a much larger time scale by applying a closed-loop MPEG encoding scheme. With this approach, the traffic enters the network at an almost constant bit rate (with a very small variation) allowing simple network management functions such as admission control and bandwidth allocation, while guaranteeing a relatively constant video quality since the encoding rate is changed only in critical periods when the shaper buffer “threatens” to overflow. The performance of the proposed scheme is evaluated through numerical tests on real video sequences View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Robust tracking enhancement of robot systems including motor dynamics: a fuzzy-based dynamic game approach

    Page(s): 538 - 552
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (636 KB)  

    A robust tracking control design of robot systems including motor dynamics with parameter perturbation and external disturbance is proposed in this study via adaptive fuzzy cancellation technique. A minimax controller equipped with a fuzzy-based scheme is used to enhance the tracking performance in spite of system uncertainties and external disturbance. The design procedure is divided into three steps. At first, a linear nominal robotic control design is obtained via model reference tracking with desired eigenvalue assignment. Next, a fuzzy logic system is constructed and then tuned to eliminate the nonlinear uncertainties as possibly as it can to enhance the tracking robustness. Finally, a minimax control scheme is specified to optimally attenuate the worst-case effect of both the residue due to fuzzy cancellation and external disturbance to achieve a minimax tracking performance. In addition, an adaptive fuzzy-based dynamic game theory is introduced to solve the minimax tracking problem. The proposed method is appropriate for the robust tracking design of robotic systems with large parameter perturbation and external disturbance. A simulation example of a two-link robotic manipulator driven by DC motors is also given to demonstrate the effectiveness of proposed design method's tracking performance View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Interval regression analysis by quadratic programming approach

    Page(s): 473 - 481
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB)  

    When we use linear programming in possibilistic regression analysis, some coefficients tend to become crisp because of the characteristic of linear programming. On the other hand, a quadratic programming approach gives more diverse spread coefficients than a linear programming one. Therefore, to overcome the crisp characteristic of linear programming, we propose interval regression analysis based on a quadratic programming approach. Another advantage of adopting a quadratic programming approach is to be able to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression. By changing the weights of the quadratic function, we can analyze the given data from different viewpoints. For data with crisp inputs and interval outputs, the possibility and necessity models can be considered. Therefore, the unified quadratic programming approach obtaining the possibility and necessity regression models simultaneously is proposed. Even though there always exist possibility estimation models, the existence of necessity estimation models is not guaranteed if we fail to assume a proper function fitting to the given data as a regression model. Thus, we consider polynomials as regression models since any curve can be represented by the polynomial approximation. Using polynomials, we discuss how to obtain approximation models which fit well to the given data where the measure of fitness is newly defined to gauge the similarity between the possibility and the necessity models. Furthermore, from the obtained possibility and necessity regression models, a trapezoidal fuzzy output can be constructed View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller

    Page(s): 449 - 463
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (680 KB)  

    Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output “small gain theorem”, the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A linguistic approach for the control of information flow in a battlefield environment

    Page(s): 588 - 595
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (284 KB)  

    We developed three linguistic statements to describe user information desires in a battlefield information environment. These rules are based on end-user interest in each track report generated from radars across the battlefield. Along with these rules of user interest, a linguistic statement describing communications systems capabilities at each node was created. These linguistic statements were converted to fuzzy variables and these variables were used as network control devices in a simulation model. The model results show that effective communications control can be exercised by these simple rules View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A transformed input-domain approach to fuzzy modeling

    Page(s): 596 - 604
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB)  

    This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Fuzzy model-based control of complex plants

    Page(s): 517 - 529
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (532 KB)  

    In the field of fuzzy modeling, the Takagi-Sugeno fuzzy model has been used to approximate accurately the dynamics of complex plants. The paper addresses two control design problems associated with state-space realizations of such fuzzy models. First, we treat the stability robustness of fuzzy model-based controllers against modeling uncertainty. Second, we develop observer-based control schemes and further investigate the behavior of estimated-state feedback. In both cases, we provide sufficient conditions that guarantee stability of the closed loop. The results are demonstrated on the fuzzy model of a gas furnace process View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • FuzzyShell: a large-scale expert system shell using fuzzy logic for uncertainty reasoning

    Page(s): 563 - 581
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (408 KB)  

    There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. However, unfortunately, much of what has been proposed can only be applied to small-scale expert systems; that is, when the number of rules is in the dozens as opposed to in the hundreds. The more traditional (nonfuzzy) expert systems are able to cope with large numbers of rules by using Rete networks for maintaining matches of all the rules and all the facts. (A Rete network obviates the need to match the rules with the facts on every cycle of the inference engine.) In this paper, we present a more general Rete network that is particularly suitable for reasoning with fuzzy logic. The generalized Rete network consists of a cascade of three networks: the pattern network, the join network, and the evidence aggregation network. The first two layers are modified versions of similar layers for the traditional Rete networks and the last, the aggregation layer, is a new concept that allows fuzzy evidence to be aggregated when fuzzy inferences are made about the same fuzzy variable by different rules View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • α-complete information in factor space

    Page(s): 553 - 562
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB)  

    In daily life, we normally describe our concepts and problems in linguistic terms. Due to the vagueness of our natural languages, the classical approach is unable to fully capture the properties (factors) of such concepts and problems and, hence, cannot provide decision-makers' complete information for making an appropriate decision. Therefore, in this paper, we first classify general fuzzy data of a problem which are presented by human linguistic terms into different categories and based on their properties, each of them is described by an appropriate fuzzy set. Then, by investigating the properties of a problem as factors of a system, a fuzzy multiobjective linear programming (FMOLP) model is proposed from the viewpoint of evidence theory and information theory to measure the uncertainty of a fuzzy problem. A learning procedure is also designed to inquire the complete information according to the required level of sufficiency α. Finally, an example of mobile phone service (MPS) is presented to show that the proposed model can aid decision-makers to identify representative (significant) factors and obtain complete information of the MPS within a few steps View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

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