2,979 publications from this institution
Abstract The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent‐based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar‐pages searching mode. Our agent‐based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity‐oriented fuzzy clustering, and knowledge‐based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.
A novel segmentation technique which may be useful for two dimensional (2D) magnetic resonance (MR) image segmentation is presented. The technique utilizes a dynamic target tracking algorithm and a Kalman filter and permits edges to be followed in the presence of intensity variation similar to that found in MR images. Segmentation of two synthetic test images, one with intensity nonuniformity and one without, is performed. Fuzzy c-means clustering with pixel intensity features is used to segment the same test images for qualitative comparison.
The identification problem which plays a central role in further development of the theory of fuzzy systems and their applications is considered. The systems under consideration are of the type XRY or (X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . . . X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> )RY, where X, X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , . . ., X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , denote fuzzy sets of input and Y stands for fuzzy set of output, while R denotes a fuzzy relation expressing the relationships between input and output. Appropriate methods of determination of fuzzy relations are presented. The idea of probabilistic sets in the identification problem is discussed in detail.
Abstract In this study, we introduce a comprehensive design methodology of hybrid fuzzy controllers (HFCs). The hybrid facet of the proposed architecture of the controller manifests in the form of a convex combination of a standard proportional integral derivative (PID) controller and a fuzzy controller. The design procedure dwells on the use of evolutionary computing (genetic algorithms) and an autotuning algorithm based on estimation modes. The tuning of the scaling factors of the HFC is an essential component of the entire optimization process. Numerical studies are presented and a detailed comparative analysis is included as well. Acknowledgments This work has been supported by EESRI (R–2003–0–285), which is funded by MOCIE (Ministry of Commerce, Industry and Energy).
This paper aims to formally define a concept of interpretability according to natural language of a logical theory, and show advances for demonstrating that the logic system called Compensatory Fuzzy Logic is interpretable. A logical theory is interpretable according to natural language if the calculus based upon the elements of this logical theory can be understood in natural language and vice versa. We present conditions necessary for a logical theory to be called interpretable, especially Compensatory Fuzzy Logic.
Changes in the Atmospheric Electric Field Signal (AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means (FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree (MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.
Double-quantitative-based granular computing implies the systematic perspective, completeness, and accuracy of rough approximation. However, most of the existing research works only focus on the case of single quantification, and there are few research study on the simultaneous computing method of double quantification. In this article, we explore feature selection with double quantification in multigranularity ordered decision systems (MG-ODSs). First, the related concepts of quantitative functions are interpreted from different viewpoints of relative and absolute quantification. Then, the multigranularity double-quantitative rough sets in an ordered decision system (ODS) from optimistic and pessimistic cases, the related properties, and three-way decisions based on the presented quantitative levels are discussed. Furthermore, the greedy algorithm for feature selection is derived. By using 12 datasets from a public repository, evaluations and comparisons are made on the parameter setting and classification accuracy. From these comparative experiments, the advantages and effectiveness of the proposed feature selection algorithm could be demonstrated over the existing approaches.
The concept of fuzzy sets is one of the most fundamental and influential tools in computational intelligence. Fuzzy sets can provide solutions to a broad range of problems of control, pattern classification, reasoning, planning, and computer vision. This book bridges the gap that has developed between theory and practice. The authors explain what fuzzy sets are, why they work, when they should be used (and when they shouldn't), and how to design systems using them. The authors take an unusual top-down approach to the design of detailed algorithms. They begin with illustrative examples, explain the fundamental theory and design methodologies, and then present more advanced case studies dealing with practical tasks. While they use mathematics to introduce concepts, they ground them in examples of real-world problems that can be solved through fuzzy set technology. The only mathematics prerequisites are a basic knowledge of introductory calculus and linear algebra. Bradford Books imprint
