IEEE Transactions on Fuzzy Systems

Fuzzy random variable is a measure function from a probability space to a collection of fuzzy variables. Based on the fuzzy random theory, this paper addresses some properties of fuzzy random renewal processes generated by a sequence of independent and identically distributed (iid) fuzzy random interarrival times. The relationship between the expected value of the fuzzy random renewal variable and... View full abstract»

The open problem recalled by Klement in the Linz2000 closing session, related to distributivity and conditional distributivity of a uninorm and a continuous t-conorm, is solved for the most usual known classes of uninorms. From the obtained results, it is deduced that distributivity and conditional distributivity are equivalent for these cases. It is remarkable that solutions appear involving not ... View full abstract»

Rough set theory has proven to be an efficient tool for modeling and reasoning with uncertainty information. By introducing probability into fuzzy approximation space, a theory about fuzzy probabilistic approximation spaces is proposed in this paper, which combines three types of uncertainty: probability, fuzziness, and roughness into a rough set model. We introduce Shannon's entropy to measure in... View full abstract»

Fuzzy discrete-event systems as a generalization of (crisp) discrete-event systems have been introduced in order that it is possible to effectively represent uncertainty, imprecision, and vagueness arising from the dynamic of systems. A fuzzy discrete-event system has been modeled by a fuzzy automaton; its behavior is described in terms of the fuzzy language generated by the automaton. In this pap... View full abstract»

This paper presents an educational library to be used with the MATLAB/Simulink package from the Mathworks company for the design of fuzzy controllers. This free library, named FlouLib, has its foundations in a clear representation of the fuzzy interfaces (fuzzification, inference, defuzzification). First, Mamdani-type conventional fuzzy logic controllers are presented, including when rulebases are... View full abstract»

This paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise. The proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine (RVM). The RVM has a probabilistic Bayesian learning framework and has good g... View full abstract»

This paper is concerned with the robust tracking control of an electrically driven robot with the model uncertainties in the robot dynamics and the motor dynamics. The motors driving the joints of the robot are assumed to be equipped with only the joint position and the current measurement devices. Adaptive fuzzy logic and adaptive backstepping method are employed to provide the solution to the co... View full abstract»

This study, for any unknown physical process y=f(x1,...,xn), is concerned with the: 1) fuzzy partition of n-dimensional input space X=X1timesmiddotmiddotmiddottimesXn into K different clusters, 2) estimating the process behavior ycirc=f(xcirc) for a given input xcirc=(xcirc1,middotmiddotmiddot,xcircn )isinX, and 3) fuzzy approximati... View full abstract»

This paper is concerned with transmission of a moving picture expert group (MPEG) video stream over a Bluetooth channel, using an adaptive neuro-fuzzy technique. MPEG variable bit rate (VBR) video sources over a network generally experience long delay and unacceptable data loss, due to high variations in bit rate. Furthermore, transmission rate could be unpredictable in a Bluetooth network due to ... View full abstract»

This paper presents implementation results using recently introduced discrete-time adaptive prediction and control techniques using online function approximators. We consider a process control experiment as our test bed, and develop a discrete-time adaptive predictor for liquid volume and a discrete-time adaptive controller for reference volume tracking. We use Takagi-Sugeno (TS) fuzzy systems as ... View full abstract»

Type-2 fuzzy sets are a generalization of the ordinary fuzzy sets in which each type-2 fuzzy set is characterized by a fuzzy membership function. In this paper, we consider the problem of ranking a set of type-2 fuzzy numbers. We adopt a statistical viewpoint and interpret each type-2 fuzzy number as an ensemble of ordinary fuzzy numbers. This enables us to define a type-2 fuzzy rank and a type-2 ... View full abstract»

We discuss the convergence of fuzzy simulation as it is employed in fuzzy optimization problems. Several convergence concepts for sequences of fuzzy variables are defined such as convergence in optimistic value. A new approach to approximating essentially bounded fuzzy variables with continuous possibility distributions is introduced. Applying the proposed approximation method to our previous work... View full abstract»

This paper presents a recursive pointwise design (RPD) method for a class of nonlinear systems represented by x(t)=f(x(t))+g(x(t))u(t). A main feature of the RPD method is to recursively design a stable controller by using pointwise information of a system. The design philosophy is that f(x(t)) and g(x(t)) can be approximated as constant vectors in very small local state spaces. Based on the desig... View full abstract»

We believe that nonlinear fuzzy filtering techniques may be turned out to give better robustness performance than the existing linear methods of estimation (H2 and H∞ filtering techniques), because of the fact that not only linear parameters (consequents), but also the nonlinear parameters (membership functions) attempt to identify the uncertain behavior of the unknown ... View full abstract»

Our focus is on the issue of decision making in risky situations. We discuss the need for using decision functions to aid in capturing the decision maker's preference among these types of uncertain alternatives. The use of fuzzy rule based formulations to model these functions is investigated. We discuss the role of perception based granular probability distributions as a means of modeling the unc... View full abstract»

Interpolative reasoning does not only help reduce the complexity of fuzzy models but also makes inference in sparse rule-based systems possible. This paper presents an interpolative reasoning method by means of scale and move transformations. It can be used to interpolate fuzzy rules involving complex polygon, Gaussian or other bell-shaped fuzzy membership functions. The method works by first cons... View full abstract»