Showing papers in "Information Sciences in 1998"

TL;DR: This work proposes reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making, and shows how to find decision rules directly from such an incomplete decision table.

TL;DR: This paper presents a framework for the formulation, interpretation, and comparison of neighborhood systems and rough set approximations using the more familiar notion of binary relations, and introduces a special class of neighborhood system, called 1-neighborhood systems.

TL;DR: This paper reviews and compares constructive and algebraic approaches in the study of rough set algebras and states axioms that must be satisfied by the operators.

TL;DR: The approach to rough set theory proposed in this paper is based on the mutual correspondence of the concepts of extension and intension, which makes it possible to formulate necessary and sufficient conditions for the existence of operations on rough sets, which are analogous to classical operations on sets.

TL;DR: This paper reviews and compares theories of fuzzy sets and rough sets, and two approaches for the formulation of fuzzy set are reviewed, one is based on many-valued logic and the other isbased on modal logic.

TL;DR: A fusion operator for numerical and linguistic information that combines linguistic values with numerical values and is used to develop a choice process for the alternatives, allowing solutions to be obtained in line with the majority of the experts' opinions.

TL;DR: A rule induction method is introduced, which extracts not only classification rules but also other medical knowledge needed for diagnosis from clinical cases, and is evaluated on three clinical databases.

TL;DR: This paper addresses the measurement of uncertainty in rough sets and rough relational databases by introducing a measurement based on information theory, and rough entropy is discussed as it applies to rough sets in general and in particular to aspects of the rough relational database model.

TL;DR: A novel approach to conflict analysis, based on rough set theory, is outlined and basic concepts of this approach are defined and analyzed.

TL;DR: A method for updating approximations of a concept incrementally is presented, using the inductive learning algorithm, LERS based on rough set theory, to implement a quasi-incremental algorithm for learning classification rules from very large data bases generalized by dynamic conceptual hierarchies provided by users.

TL;DR: The architecture employs an approximation to the fuzzy reasoning system to considerably reduce the dimensions of the network as compared to similar approaches, demonstrating the advantage of the neurofuzzy approach and highlighting the advantages of the architecture for hardware realisations.

TL;DR: It is shown that all algorithms can be executed efficiently on the LARPBS model, which is a linear array with a reconfigurable pipelined bus system based on current optical technologies.

TL;DR: This paper deals with the Cauchy problem of fuzzy differential equations, and the existence theorems under compactness-type conditions are obtained.

TL;DR: A new pavement distress image enhancement algorithm, and a new analysis and classification algorithm that can precisely quantify geometrical and topological parameters, can quickly accept new classification rules for classification, and can estimate the distress severity from the thresholded image are introduced.

TL;DR: This paper provides a numerical method to approximate the improper fuzzy Riemann integral by invoking the Simpson's rule and fits the end points (closed interval) of the α-level set of the improper fuzziness RiemANN integral as polynomials with variable α in a least-squares sense.

TL;DR: This paper presents a spectrum of minimax models as opposed to maximax models based on chance constrained programming as well as chance constrained multi-objective programming and chance constrained goal programming, in which the minimax model will select the alternative that provides the best of the worst possible return.

TL;DR: This paper deals with problems which arise if for well justified statistical models like linear regression only fuzzy data are available, and an application of Zadeh's extension principle to optimal classical estimators finds that they do not keep their optimality properties.

TL;DR: A new filtering algorithm is developed, called Fuzzy Kalman Filtering (FKF), which preserves the same recursive mechanism of the KF and IKF, but produces a scalar-valued (rather than an interval-valued) estimate at each iteration of the filtering process.

TL;DR: It is shown that the introduction of rough and fuzzy semantic structures in neural networks can increase the accuracy of predictions.

TL;DR: It is constructively proved that the general MISO TS fuzzy systems with the simplified linear rule consequent are universal approximators and can approximate any continuous function in closed domain arbitrarily well.

TL;DR: This paper presents an enhancement to the fuzzy object-oriented data model (FOOD) that permits a truer representation of different types of uncertainty, and describes a software architecture for the implementation of the model.

TL;DR: It is shown that a prescriptive learning procedure where the weights are simply read off based on the training data can provide good generalization.

TL;DR: In this paper, the authors describe the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture with the aim of acquiring computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost.

TL;DR: A practical specific (r, n) threshold scheme for secret digital images that divides a secret digital image into n pieces and distribute them to n participants achieves perfect secrecy.

TL;DR: The PPR model shows that both knowledge-intensive CBR systems and the nearest neighbor algorithms share a common core formalism and that their difference is on whether or not (respectively) they use a general theory in addition to the precedent cases.

TL;DR: Hardware implementations of fuzzy systems, neural networks and fuzzy neural networks (FNNs) using Xilinx Field Programmable Gate Arrays (FPGAs) are described and the validity of these approaches is demonstrated by their application to a non-linear function approximation problem.

TL;DR: Work on the fuzzy topology and geometry of image subsets, including adjacency, separation, and connectedness; distance and relative position; area, perimeter, and diameter; convexity; medial axes and thinning; as well as some applications of these concepts in image processing and analysis are reviewed.

TL;DR: This paper shows how to treat the operations of fuzzy numbers with step form by using Function Principle.

TL;DR: An FLC for controlling the power level of a nuclear reactor is proposed and first tested by comparing it with the classical controller of BR1 (Belgium's first research reactor) to develop an optimised and intrinsically safe controller.

TL;DR: The emergence of the cooperative behavior for communicating agents by means of Genetic Programming is presented and the effectiveness of the emergent communication in terms of the robustness of generated GP programs is shown.