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  1. A Fuzzy Method for Learning Simple Boolean Formulas from Examples

    We discuss a method for inferring Boolean functions from examples. The method is inherently fuzzy in two respects: i) we work with a pair of formulas...
    Bruno Apolloni, Andrea Brega, ... Anna Maria Zanaboni in Computational Intelligence for Modelling and Prediction
    Chapter
  2. Connectionist Fuzzy Relational Systems

    There have been an enormous number of fuzzy systems developed so far. For excellent surveys the reader is referred to [1][3][5][7][9][10][18][19]....
    Rafał Scherer, Leszek Rutkowski in Computational Intelligence for Modelling and Prediction
    Chapter
  3. Fuzzy Logic Control in Hybrid Power Systems

    In this chapter the dynamic analysis of a hybrid power system, employing diesel and locally available wind and solar energies, is discussed and...
    Josiah Munda, Sadao Asato, Hayao Miyagi in Computational Intelligence for Modelling and Prediction
    Chapter
  4. Evolution of the Internet Topology and Traffic Dynamics of Data Packets

    In recent years, the Internet has become one of the most influential media in our daily life, going beyond in its role as the basic infrastructure in...
    Kwang-Il Goh, Byungnam Kahng, Doochul Kim in Complex Dynamics in Communication Networks
    Chapter
  5. Data Mining and User Profiling for an E-Commerce System

    Many companies are now develo** an online internet presence to sell or promote their products and services. The data generated by e-commerce sites...
    Ken McGarry, Andrew Martin, Dale Addison in Classification and Clustering for Knowledge Discovery
    Chapter
  6. Ontology-based Fuzzy Decision Agent and Its Application to Meeting Scheduling Support System

    A Fuzzy Decision Agent (FDA) based on personal ontology for Meeting Scheduling Support System (MSSS) is proposed in this chapter. In this system,...
    Chang-Shing Lee, Hei-Chia Wang, Meng-Ju Chang in Classification and Clustering for Knowledge Discovery
    Chapter
  7. D-GridMST: Clustering Large Distributed Spatial Databases

    In this paper, we will propose a novel distributable clustering algorithm, called Distributed-GridMST (D–GridMST for short), which deals with large...
    Chapter
  8. A Probabilistic Approach to Mining Fuzzy Frequent Patterns

    Deriving association rules is a typical task in data mining. The problem was originally defined for transactions of discrete items, but it was soon...
    Attila Gyenesei, Jukka Teuhola in Classification and Clustering for Knowledge Discovery
    Chapter
  9. Data Mining of Missing Persons Data

    This paper presents the results of analysis to evaluate the effectiveness of data mining techniques to predict the outcome for missing persons cases....
    K. Blackmore, T. Bossomaier, ... D. Thomson in Classification and Clustering for Knowledge Discovery
    Chapter
  10. 9. Switched Scalable Video Coding Systems

    Scalable video coding means that a video sequence is only encoded once and the coded bitstream has the capability of sending any sub-bitstream...
    Zhengguo Li, Yengchai Soh, Changyun Wen in Switched and Impulsive Systems
    Chapter
  11. 4. Stability of Nonlinear Switched and Impulsive Systems

    In this chapter, we shall study the stability of nonlinear switched and impulsive systems of the form...
    Zhengguo Li, Yengchai Soh, Changyun Wen in Switched and Impulsive Systems
    Chapter
  12. Marginal Filter Stability

    Two fundamental aspects in the use of the Kalman Filter for simultaneous localization and map** are the issues of observability and...
    Juan Andrade-Cetto, Alberto Sanfeliu in Environment Learning for Indoor Mobile Robots
    Chapter
  13. C Sigma Points

    A set of p + 1 sigma points $\mathcal{S} = \{\mathcal{X}^i, W^i\}$...
    Juan Andrade-Cetto, Alberto Sanfeliu in Environment Learning for Indoor Mobile Robots
    Chapter
  14. Soft Computing Paradigms for Web Access Pattern Analysis

    Web servers play a crucial role to convey knowledge and information to the end users. With the popularity of the WWW, discovering the hidden...
    **aozhe Wang, Ajith Abraham, Kate A. Smith in Classification and Clustering for Knowledge Discovery
    Chapter
  15. A Longitudinal Comparison of Supervised and Unsupervised Learning Approaches to Iso-Resource Grou** for Acute Healthcare in Australia

    Estimating resource consumption of hospital patients is important for various tasks such as hospital funding, and management and allocation of...
    Eu-Gene Siew, Kate A. Smith, ... Jeff Wassertheil in Classification and Clustering for Knowledge Discovery
    Chapter
  16. 1. Examples and Modelling of Switched and Impulsive Systems

    When you begin to read this book, you may ask: “what is a switched and impulsive system?” This is a question that may be best answered through...
    Zhengguo Li, Yengchai Soh, Changyun Wen in Switched and Impulsive Systems
    Chapter
  17. Synchronization in Complex Networks

    The study of complex systems pervades all of science, from cell biology to ecology, from computer science to meteorology. A paradigm of a complex...
    Ljupco Kocarev, Gábor Vattay in Complex Dynamics in Communication Networks
    Chapter
  18. Data Traffic, Topology and Congestion

    We consider the interaction between the topology of a network and the tra.c carried along its channels. The binding elements between the topology and...
    David K. Arrowsmith, R.J. Mondrag, M. Woolf in Complex Dynamics in Communication Networks
    Chapter
  19. The Many Faces of a Kohonen Map A Case Study: SOM-based Clustering for On-Line Fraud Behavior Classification

    The Self-Organizing Map (SOM) is an excellent tool for exploratory data analysis. It projects the input space on prototypes of a low-dimensional...
    Chapter
  20. Use of Fuzzy Feature Descriptions to Recognize Handwritten Alphanumeric Characters

    The main challenge in handwritten character recognition involves the development of a method that can generate descriptions of the handwritten...
    G.E.M.D.C. Bandara, R.M. Ranawana, S.D. Pathirana in Classification and Clustering for Knowledge Discovery
    Chapter
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