Advertisement

Artificial Organic Networks

Artificial Intelligence Based on Carbon Networks

  • Hiram Ponce-Espinosa
  • Pedro Ponce-Cruz
  • Arturo Molina

Part of the Studies in Computational Intelligence book series (SCI, volume 521)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina
    Pages 1-30
  3. Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina
    Pages 31-52
  4. Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina
    Pages 53-72
  5. Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina
    Pages 73-111
  6. Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina
    Pages 113-129
  7. Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina
    Pages 131-153
  8. Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina
    Pages 155-189
  9. Back Matter
    Pages 191-228

About this book

Introduction

This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular.

The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described:

·        approximation;

·        inference;

·        clustering;

·        control;

·        classification; and

·        audio-signal filtering.

The text finishes with a consideration of directions in which AHNs  could be implemented and developed in future. A complete LabVIEW™ toolkit, downloadable from the book’s page at springer.com enables readers to design and implement organic neural networks of their own.

The novel approach to creating networks suitable for machine learning systems demonstrated in Artificial Organic Networks will be of interest to academic researchers and graduate students working in areas associated with computational intelligence, intelligent control, systems approximation and complex networks.

Keywords

Artificial Systems Computational Algorithms LabVIEW™ Machine Learning Nonlinear Approximators Organic Methods Organic Networks

Authors and affiliations

  • Hiram Ponce-Espinosa
    • 1
  • Pedro Ponce-Cruz
    • 2
  • Arturo Molina
    • 3
  1. 1.Campus Ciudad de MéxicoInstituto Tecnológico de Estudios Superiores de MonterreyTlalpanMexico
  2. 2.Campus Ciudad de MéxicoInstituto Tecnológico de Estudios Superiores de MonterreyTlalpanMexico
  3. 3.campus Ciudad de MéxicoInstituto Tecnológico de Estudios Superiores de MonterreyTlalpanMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-02472-1
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-02471-4
  • Online ISBN 978-3-319-02472-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
Industry Sectors
Pharma
Automotive
Chemical Manufacturing
Biotechnology
Finance, Business & Banking
Electronics
IT & Software
Telecommunications
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering