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Computational Intelligence Based on Lattice Theory

  • Book
  • © 2007

Overview

Editors:
  1. Vassilis G. Kaburlasos

    1. Technological Educational Institution of Kavala, Kavala, Greece

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    You can also search for this editor in PubMed   Google Scholar

  2. Gerhard X. Ritter

    1. University of Florida, Gainesville, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Resent results of Computational Intelligence Based on Lattice Theory
  • Outcome of a special session held during in WCCI 2006
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 67)

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Neural Computation

    1. Front Matter

      Pages 2-2
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    2. Granular Enhancement of Fuzzy ART/SOM Neural Classifiers Based on Lattice Theory

      • Vassilis G. Kaburlasos
      Pages 3-23

    3. Learning in Lattice Neural Networks that Employ Dendritic Computing

      • Gerhard X. Ritter, Gonzalo Urcid
      Pages 25-44

    4. Orthonormal Basis Lattice Neural Networks

      • Angelos Barmpoutis, Gerhard X. Ritter
      Pages 45-58

    5. Generalized Lattices Express Parallel Distributed Concept Learning

      • Michael J. Healy, Thomas P. Caudell
      Pages 59-77

  3. Mathematical Morphology Applications

    1. Front Matter

      Pages 80-80
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    2. Noise Masking for Pattern Recall Using a Single Lattice Matrix Associative Memory

      • Gonzalo Urcid, Gerhard X. Ritter
      Pages 81-100

    3. Convex Coordinates From Lattice Independent Sets for Visual Pattern Recognition

      • Manuel Graña, Ivan Villaverde, Ramon Moreno, Francisco X. Albizuri
      Pages 101-128

    4. A Lattice-Based Approach to Mathematical Morphology for Greyscale and Colour Images

      • Valérie De Witte, Stefan Schulte, Mike Nachtegael, Tom Mélange, Etienne E. Kerre
      Pages 129-148

    5. Morphological and Certain Fuzzy Morphological Associative Memories for Classification and Prediction

      • Peter Sussner, Marcos Eduardo Valle
      Pages 149-171

  4. Machine Learning Applications

    1. Front Matter

      Pages 174-174
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    2. The Fuzzy Lattice Reasoning (FLR) Classifier for Mining Environmental Data

      • Ioannis N. Athanasiadis
      Pages 175-193

    3. Machine Learning Techniques for Environmental Data Estimation

      • Vassilios Petridis, Vassilis Syrris
      Pages 195-214

    4. Application of Fuzzy Lattice Neurocomputing (FLN) in Ocean Satellite Images for Pattern Recognition

      • J. A. Piedra-Fernández, M. Cantón-Garbín, F. Guindos-Rojas
      Pages 215-232

    5. Genetically Engineered ART Architectures

      • Ahmad Al-Daraiseh, Assem Kaylani, Michael Georgiopoulos, Mansooreh Mollaghasemi, Annie S. Wu, Georgios Anagnostopoulos
      Pages 233-262

    6. Fuzzy Lattice Reasoning (FLR) Classification Using Similarity Measures

      • Al Cripps, Nghiep Nguyen
      Pages 263-284

  5. Logic and Inference

    1. Front Matter

      Pages 286-286
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    2. Fuzzy Prolog: Default Values to Represent Missing Information

      • Susana Munoz-Hernandez, Claudio Vaucheret
      Pages 287-308

    3. Valuations on Lattices: Fuzzification and its Implications

      • Kevin H. Knuth
      Pages 309-324
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Keywords

About this book

A number of di?erent instruments for design can be uni?ed in the context of lattice theory towards cross-fertilization By“latticetheory”[1]wemean,equivalently,eitherapartialordering relation [2,3]ora couple of binary algebraic operations [3, 4]. There is a growing interest in computational intelligence based on lattice theory. A number of researchers are currently active developing lattice theory based models and techniques in engineering, computer and information s- ences, applied mathematics, and other scienti?c endeavours. Some of these models and techniques are presented here. However, currently, lattice theory is not part of the mainstream of com- tationalintelligence.Amajorreasonforthisisthe“learningcurve”associated with novel notions and tools. Moreover, practitioners of lattice theory, in s- ci?c domains of interest, frequently develop their own tools and/or practices without being aware of valuable contributions made by colleagues. Hence, (potentially) useful work may be ignored, or duplicated. Yet, other times, di?erent authors may introduce a con?icting terminology. The compilation of this book is an initiative towards proliferating est- lished knowledge in the hope to further expand it, soundly. There was a critical mass of people and ideas engaged to produce this book. Around two thirds of this book’s chapters are substantial enhancements of preliminary works presented lately in a three-part special session entitled “Computational Intelligence Based on Lattice Theory” organized in the c- text of the World Congress in Computational Intelligence (WCCI), FUZZ- IEEE program, July 16-21, 2006 in Vancouver, BC, Canada. The remaining book chapters are novel contributions by other researchers.

Editors and Affiliations

  • Technological Educational Institution of Kavala, Kavala, Greece

    Vassilis G. Kaburlasos

  • University of Florida, Gainesville, USA

    Gerhard X. Ritter

Bibliographic Information

  • Book Title: Computational Intelligence Based on Lattice Theory

  • Editors: Vassilis G. Kaburlasos, Gerhard X. Ritter

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-72687-6

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2007

  • Hardcover ISBN: 978-3-540-72686-9Published: 24 July 2007

  • Softcover ISBN: 978-3-642-09174-2Published: 25 November 2010

  • eBook ISBN: 978-3-540-72687-6Published: 26 June 2007

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XVI, 375

  • Topics: Artificial Intelligence, Mathematical and Computational Engineering

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