Showing papers in "International Journal of Approximate Reasoning in 1999"

TL;DR: The result shows that the approach developed is simple and comprehensible in concept, efficient in computation, and robust and flexible in modeling the human evaluation process, thus making it of general use for solving practical MA problems.

TL;DR: The behaviour of a general reasoning method is described, six proposals for this general model are analyzed, and a method to learn the parameters of these FRMs by means of Genetic Algorithms is presented, adapting the inference mechanism to the set of rules.

TL;DR: In this article, the authors present a probability scale that contains words as well as numbers to help users understand the output of a belief network and provide an aid for researchers and domain experts during the elicitation phase.

TL;DR: This paper extends the theory of belief functions by introducing new concepts and techniques, allowing to model the situation in which the beliefs held by a rational agent may only be expressed (or are only known) with some imprecision.

TL;DR: Recent work focusing on the use of Takagi–Sugeno fuzzy models in combination with MBPC is described, including a branch-and-bound method with iterative grid-size reduction and control based on a local linear model.

TL;DR: The results of this study show that the proposed genetic-fuzzy approach can produce efficient knowledge base of an FLC for controlling the motion of a robot among moving obstacles.

TL;DR: Using a parametric Riemann integral representation, a numerical algorithm for solving fuzzy Fredholm and Voltera integral equations of the second kind with arbitrary kernel is proposed and illustrated with examples.

TL;DR: A new model, the Probabilistic Temporal Network (PTN), is explored for representing temporal and atemporal information while fully embracing probabilistic semantics, which allows representation of time constrained causality, of when and if events occur, and of the periodic and recurrent nature of processes.

TL;DR: It is concluded that probabilistic deduction by the iterative application of inference rules on interval restrictions for conditional probabilities, even though considered very promising in the literature so far, is very limited in its field of application.

TL;DR: The construction of evaluation functions to compare alternatives in decision making under uncertainty is the central focus of this work and evaluation functions are developed which allow for the inclusion of both probabilistic information and attitudinal predilections held by the decision maker.

TL;DR: The piecewise-affine fuzzy model structure is used as non-linear prototype for a multi–input, single–output unknown system and the consequents of the fuzzy model are identified from noisy data which are collected from experiments on the real system.

TL;DR: This work surveys the rules that have been proposed for defining conditional possibilities and investigates which of them satisfy the consistency criteria in two situations of practical interest, and introduces a new rule that is more informative and is also coherent in both cases.

TL;DR: The paper presents a graphical characterization of the largest chain graphs which serve as unique representatives of classes of Markov equivalent chain graphs, and is a basis for an algorithm constructing, for a given chain graph, thelargest chain graph equivalent to it.

TL;DR: A hierarchical control strategy consisting on a supervisory switching of PID controllers, simplified using the c-Means clustering technique, is developed and applied to the distributed collector field of a solar power plant.

TL;DR: In this paper, the problem of combining imprecise and uncertainty information from the approximate reasoning point of view is addressed, and a method for combining them based on the use of information measures is proposed.

TL;DR: The modal logic interpretation of plausibility and belief measures on an arbitrary universe of discourse, as proposed by Harmanec et al., is further developed by employing notions from set-valued analysis.

TL;DR: This work proposes a procedure to design adaptive and self-learning fuzzy controllers in real time, requiring only a limited prior knowledge of the plant to be controlled, both in terms of the quantity and precision of this information.

TL;DR: A synthesis of the study proposed can constitute a base to choose an algorithm in order to apply it to a process diagnosis in real time and shows how the unsupervised and supervised methods can associate in a diagnosis algorithm.

TL;DR: General relationships among negations, convex Archimedean nilpotent t-norms, and automorphisms of the unit interval I are explored.

TL;DR: This paper presents a general language for representing subsets of a cartesian product of finite sets, and presents three different symbloic methods for computing the probability of a formula in the language without explicitly constructing the corresponding system states.

TL;DR: The overall contour skid-steer model (OCSM) is developed, followed by the design of the fuzzy logic (FL) controllers for SP, which is the main focus of the work.

TL;DR: Findings from the experiment show that the neuro-fuzzy classifier can effectively model subjective classification problem-solving knowledge from a small set of examples, and it can represent the formulated models as a concise and intuitive set of fuzzy rules.

TL;DR: This paper defines different logics of preference by considering the comparisons of possibility measures and guaranteed possibility measures, and some properties of the proposed logics are studied.

TL;DR: If the data is properly transformed before the identification process, the resulting fuzzy model can be improved to the point it may not need a further tuning, according to the significance of the data transform.

TL;DR: A fuzzy projection pursuit density estimation based on the membership function and the eigenvector of the covariance matrix and Marginal densities along the subspace spanned by the projection vector are estimated.

TL;DR: G_DACG combines the powerful optimisation capabilities of genetic programming with a novel and cheap fitness function, which relies on the semantic separation of concepts expressed in terms of Cartesian granule fuzzy sets, in identifying these additive models.

TL;DR: It is shown that fuzzy sets can be interpreted as the aggregation of a set of observations and formalized by means of the OWA and the WOWA operators.

TL;DR: This work proposes an approach in which all models are utilized with the task of getting a system which performes better than the best available model, and different mathematical models of multiphase flow rate estimation and neural models co-operate by using a meta-decision maker based on fuzzy theory.

TL;DR: It is shown that the concept of agreement provides a framework for the development of a formal and sound explanation for concepts which lack formal semantics, and the semantics of the logic of agreement can be seen as being similar to a semantics of possible worlds, one for each individual agent.