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  1. Fuzzy Estimation

    sThe first thing to do is explain how we will get fuzzy numbers, and fuzzy probabilities, from a set of confidence intervals which will be...
    James J. Buckley in Simulating Fuzzy Systems
    Chapter

  2. Voice Recognition with Neural Networks, Fuzzy Logic and Genetic Algorithms

    We describe in this chapter the use of neural networks, fuzzy logic and genetic algorithms for voice recognition. In particular, we consider the case...
    Patricia Melin, Oscar Castillo in Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
    Chapter

  3. Application of Evolutionary Algorithms to Global Cluster Geometry Optimization

    This contribution focuses upon the application of evolutionary algorithms to the nondeterministic polynomial hard problem of global cluster geometry...
    Bernd Hartke in Applications of Evolutionary Computation in Chemistry
    Chapter

  4. Prediction of Crystal Structures Using Evolutionary Algorithms and Related Techniques

    Methods, evolutionary and systematic search approaches, and applications of crystal structure prediction of closest-packed and framework materials...
    Scott M. Woodley in Applications of Evolutionary Computation in Chemistry
    Chapter

  5. Fuzzy Subsets and Fuzzy Subgroups

    The pioneering work of Zadeh on fuzzy subsets of a set in [53] and Rosenfeld on fuzzy subgroups of a group in [43] led to the fuzzification of...
    John N. Mordeson, Kiran R. Bhutani, Azriel Rosenfeld in Fuzzy Group Theory
    Chapter

  6. Fuzzy Caley's Theorem and Fuzzy Lagrange's Theorem

    We begin our discussion with properties of normal fuzzy subgroups. Fuzzy analogs of some group theoretic concepts such as cosets, characteristic...
    John N. Mordeson, Kiran R. Bhutani, Azriel Rosenfeld in Fuzzy Group Theory
    Chapter

  7. Fuzzy Subgroups of Abelian Groups

    Some of the best examples of algebraic structure theory come from commutative group theory. Commutative group theory is also a principal reason for...
    John N. Mordeson, Kiran R. Bhutani, Azriel Rosenfeld in Fuzzy Group Theory
    Chapter

  8. Random Voronoi Ensembles for Gene Selection in DNA Microarray Data

    Currently, cancer and other complex pathologies are analyzed mainly by morphological classification. In the past few decades there have been dramatic...
    Francesco Masulli, Stefano Rovetta in Bioinformatics Using Computational Intelligence Paradigms
    Chapter

  9. Cancer Classification with Microarray Data Using Support Vector Machines

    Microarrays (Schena et al. 1995) are also called gene chips or DNA chips. On a microarray chip, there are thousands of spots. Each spot contains the...
    Feng Chu, Lipo Wang in Bioinformatics Using Computational Intelligence Paradigms
    Chapter

  10. Class Prediction with Microarray Datasets

    Microarray technology is having a significant impact in the biological and medical sciences and class prediction will play an increasingly important...
    Simon Rogers, Richard D. Williams, Colin Campbell in Bioinformatics Using Computational Intelligence Paradigms
    Chapter

  11. Structural Models

    The goal of structural modelling is to define all signal paths in a system.1 So for every possible signal path, the structural model has to determine...
    Thomas Steffen in Control Reconfiguration of Dynamical Systems
    Chapter

  12. General Reconfiguration Problem

    In this chapter, a formal definition of the reconfiguration problem is developed. While the general idea of reconfiguration may appear obvious, there...
    Thomas Steffen in Control Reconfiguration of Dynamical Systems
    Chapter

  13. Basic Structural Properties

    It is possible to attribute properties to a system structure. These properties are called structural properties, and they hold for almost all systems...
    Thomas Steffen in Control Reconfiguration of Dynamical Systems
    Chapter

  14. Reconfiguration of the 3-Tank System

    The 3-Tank experiment is shown in Fig. 16.1. It consists of three tanks, which are connected via the valves u2, u3 and u4. Pumps can bring water into...
    Thomas Steffen in Control Reconfiguration of Dynamical Systems
    Chapter

  15. A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle

    In molecular biology, functions are produced by a set of macromolecules that interact at different levels. Genes and their products, proteins,...
    Florence d’Alché-Buc, Pierre-Jean Lahaye, ... Samuele Bottani in Bioinformatics Using Computational Intelligence Paradigms
    Chapter

  16. Support Vector Machines for Signal Processing

    This chapter deals with the use of the support vector machine (SVM) algorithm as a possible design method in the signal processing applications. It...
    D. Mattera in Support Vector Machines: Theory and Applications
    Chapter

  17. Theoretical and Practical Model Selection Methods for Support Vector Classifiers

    In this chapter, we revise several methods for SVM model selection, deriving from different approaches: some of them build on practical lines of...
    D. Anguita, A. Boni, ... D. Sterpi in Support Vector Machines: Theory and Applications
    Chapter

  18. Epilogue – Towards Develo** A Realistic Sense of Artificial Intelligence

    So far, we have considered how the artificial mind system based upon the holistic model as depicted in Fig. 5.1 (on page 84) works in terms of the...
    Tetsuya Hoya in Artificial Mind System - Kernel Memory Approach
    Chapter

  19. A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set

    A correct selection of features (attributes) is vital in data mining. For this aim, the complete set of features is constructed. Here are some...
    T.Y. Lin in Foundations and Advances in Data Mining
    Chapter

  20. Incremental Mining on Association Rules

    The discovery of association rules has been known to be useful in selective marketing, decision analysis, and business management. An important...
    W.-G. Teng, M.-S. Chen in Foundations and Advances in Data Mining
    Chapter
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