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  1. Fingerprint Recognition with Modular Neural Networks and Fuzzy Measures

    We describe in this chapter a new approach for fingerprint recognition using modular neural networks with a fuzzy logic method for response...
    Patricia Melin, Oscar Castillo in Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
    Chapter

  2. Modular Neural Networks

    We describe in this chapter the basic concepts, theory and algorithms of modular and ensemble neural networks. We will also give particular attention...
    Patricia Melin, Oscar Castillo in Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
    Chapter

  3. Type-1 Fuzzy Logic

    This chapter introduces the basic concepts, notation, and basic operations for the type-1 fuzzy sets that will be needed in the following chapters....
    Patricia Melin, Oscar Castillo in Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
    Chapter

  4. Human Recognition using Face, Fingerprint and Voice

    We describe in this chapter a new approach for human recognition using as information the face, fingerprint, and voice of a person. We have described...
    Patricia Melin, Oscar Castillo in Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing
    Chapter

  5. Project Network Model

    The project network diagram is in Fig. 26.1. This problem is modelled after an example in [2]. The project consists of various jobs that must be...
    James J. Buckley in Simulating Fuzzy Systems
    Chapter

  6. Simulation

    Now we come to the point were we need to select simulation software to do all the crisp simulations staring in Chap. 7. The author is not an expert...
    James J. Buckley in Simulating Fuzzy Systems
    Chapter

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  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. Nonlinear Instabilities in TCP-RED

    With the growing size and popularity of the Internet, congestion control has emerged as an important problem. Poor management of congestion can...
    Priya Ranjan, Eyad H. Abed, Richard J. La in Complex Dynamics in Communication Networks
    Chapter

  17. On Dynamics of Transport Protocols Over Wide-Area Internet Connections

    The Transport Control Protocol (TCP) is widely deployed over the Internet for reliably transporting data [27], and it accounts for a significant...
    Nageswara S.V. Rao, Jianbo Gao, Leon O. Chua in Complex Dynamics in Communication Networks
    Chapter

  18. 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

  19. 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

  20. 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
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