2,979 publications from this institution
A design method for a hierarchical control structure is introduced. The structure involves two different levels of control, namely, a coordination level and an execution level. At the coordination level a fuzzy controller is used to identify the status of the system under control and to activate appropriate local control modules, located at the execution level. The design of the fuzzy system is based on the control requirements and on the response of the local control elements. Simulation results show that the controller can satisfy the control objective.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
The underlying intent of this study is to show how numeric data, fuzzy sets (and information granules, in general) as well as information granules of higher type build a knowledge-based conceptual hierarchy. The bottom-up organization of the paper starts with a concept and selected techniques of data compactification. Compactification is the process, which involves information granulation and in successive phases may give rise to higher type constructs (say, type-2 fuzzy sets, interval-valued fuzzy sets and alike). The detailed algorithmic investigations are provided where we show how membership grades of higher type constructs are formed. In the sequel, we focus on computing with words (CW) which in this context is regarded as a general paradigm of processing information granules. We stress the relationships between numeric computing and processing information granules of well-defined semantics (which constitutes the essence of computing with words).
Meteorological volumetric radar data is used to detect thunderstorms that are responsible for most severe summer weather. Discriminating between different storm types is a difficult problem, however. A radar data processing system conducts a volume scan by stepping a continuously rotating antenna through a series of elevation angles at regular intervals. Systems exist that allow meteorologists to focus their attention on regions of interest within the radar scan known as storm cells. When a cell is found, a number of derived features are computed. Each cell is also assigned a storm type label as determined by ground observers of the actual storm. Many of these products are computed using rules that incorporate threshold values. By varying these thresholds, the requisite features may be modified for each cell. We propose using a genetic algorithm to determine optimal values for the thresholds based on the labelling of the cells. We may evaluate the performance of the threshold values using linear discriminant analysis (LDA) on the derived features by comparing the computed and desired output of each cell. Once the optimized thresholds have been determined, we compare the classification performance of LDA, trained using the cell features generated using the new thresholds, against LDA trained with the original cell features.
We firstly review some fundamentals of fuzzy relation calculus and, by recalling some known results, we improve the mathematical contents of our previous papers by using the properties of a triangular norm over [0,1]. We make wide use of the theory of fuzzy relation equations for getting lossy compression and decompression of images interpreted as two-argument fuzzy matrices.The same scope is achieved by decomposing a fuzzy matrix using the concept of Schein rank. We illustrate two algorithms with a few examples.
In this article, we study how to manage the consistency and consensus in group decision-making (GDM) with hesitant multiplicative preference relations (HMPRs). First, an approach to develop the priority weight vector of an HMPR is presented. Then, the consistency index of an HMPR is defined for its consistency checking. Subsequently, we define an acceptable consistent HMPR and propose an iterative procedure to improve the consistency of an unacceptable consistent HMPR. As to the GDM with HMPRs, distance measures and proximity degrees are defined to derive the weights of the decision makers (DMs). Moreover, a consensus index is proposed for measuring the agreement degree of different DMs' judgments, and an iterative approach is offered to implement the consensus reaching process. In the sequel, an algorithm to GDM with HMPRs is put forward. Two numerical examples are covered to highlight the merits of the proposed algorithms.
In this study, we introduce a concept of granular worlds and elaborate on various representation and communication issues arising therein. A granular world embodies a collection of information granules being regarded as generic conceptual entities used to represent knowledge and handle problem solving. Granular computing is a paradigm supporting knowledge representation, coping with complexity, and facilitating interpretation of processing. In this sense, it is crucial to all man-machine pursuits and data mining and intelligent data analysis, in particular. There are two essential facets that are inherently associated with any granular world, that is a formalism used to describe and manipulate information granules and the granularity of the granules themselves (roughly speaking, by the granularity we mean a "size" of such information granules; its detailed definition depends upon the formal setting of the granular world). There are numerous formal models of granular worlds ranging from set-theoretic developments (including sets, fuzzy sets, and rough sets) to probabilistic counterparts (random sets, random variables and alike). In light of the evident diversity of granular world (occurring both in terms of the underlying formal settings as well as levels of granularity), we elaborate on their possible interaction and identify implications of such communication. More specifically, we have cast these in the form of the interoperability problem that is associated with the representation of information granules. © 2000 John Wiley & Sons, Inc.
Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.
Location management (LM) is an essential feature in a personal communication service (PCS) network composed of a group of base stations. LM enables the PCS network to find the current cell, the radio coverage of a base station, in which a mobile terminal (MT) resides when an incoming call to the MT arrives. LM involves two operations: location update and paging. This study aims to develop algorithms that reduce paging cost so that total LM cost is reduced. As an optimization technique we consider the use of genetic algorithms using which we develops a sequential paging scheme. Furthermore, numeric studies are presented so that we can compare the proposed paging scheme with some well-known sequential paging schemes such as reverse paging, semi-reverse paging, and uniform paging. The experimental results demonstrate the effectiveness of the proposed paging scheme (minimal paging cost) over the existing ones.
In this study, we highlight some fundamental issues of knowledge management and cast them in the setting of Granular Computing (GrC). We show how its formal constructs — information granules are instrumental in knowledge representation and specification of its level of abstraction.
