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Artificial Organic Networks Artificial Intelligence Based on Carbon Networks /
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 desig...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
|
| Edición: | 1st ed. 2014. |
| Colección: | Studies in Computational Intelligence,
521 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-3-319-02472-1 | ||
| 003 | DE-He213 | ||
| 005 | 20220116024719.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 131112s2014 sz | s |||| 0|eng d | ||
| 020 | |a 9783319024721 |9 978-3-319-02472-1 | ||
| 024 | 7 | |a 10.1007/978-3-319-02472-1 |2 doi | |
| 050 | 4 | |a Q342 | |
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| 072 | 7 | |a UYQ |2 thema | |
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| 100 | 1 | |a Ponce-Espinosa, Hiram. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Artificial Organic Networks |h [electronic resource] : |b Artificial Intelligence Based on Carbon Networks / |c by Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina. |
| 250 | |a 1st ed. 2014. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2014. | |
| 300 | |a XII, 228 p. 192 illus., 56 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 521 | |
| 505 | 0 | |a Introduction to Modeling Problems -- Chemical Organic Compounds -- Artificial Organic Networks -- Artificial Hydrocarbon Networks -- Enhancements of Artificial Hydrocarbon Networks -- Notes on Modeling Problems Using Artificial Hydrocarbon Networks -- Applications of Artificial Hydrocarbon Networks.-Appendices. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Biotechnology. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 1 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Chemical Bioengineering. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 700 | 1 | |a Ponce-Cruz, Pedro. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Molina, Arturo. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319024738 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319024714 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319378008 |
| 830 | 0 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 521 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-02472-1 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||