Descripción del título
Artificial Organic 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 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 complexnetworks
Monografía
monografia Rebiun36053606 https://catalogo.rebiun.org/rebiun/record/Rebiun36053606 m o d | cr nn 008mamaa 131112s2014 gw s 00 0 eng d 9783319024721 9783319024738 9783319024714 9783319378008 10.1007/978-3-319-02472-1 doi UMA.RE eng UYQ bicssc COM004000 bisacsh 006.3 23 Ponce-Espinosa, Hiram. aut. http://id.loc.gov/vocabulary/relators/aut Artificial Organic Networks Recurso electrónico] Artificial Intelligence Based on Carbon Networks by Hiram Ponce-Espinosa, Pedro Ponce-Cruz, Arturo Molina Cham Springer International Publishing 2014 Cham Cham Springer International Publishing Cham Springer International Publishing Imprint: Springer 2014 Cham Cham Springer International Publishing Imprint: Springer XII, 228 p. 192 il., 56 il. col XII, 228 p. 192 il., 56 il. col Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Computational Intelligence 521 Bibliographic Level Mode of Issuance: Monograph 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 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 complexnetworks English Engineering Artificial intelligence Biochemical engineering Computer simulation Computational Intelligence. Artificial Intelligence. Biochemical Engineering. Simulation and Modeling. Engineering Artificial intelligence Biochemical engineering Computer simulation Computational Intelligence Artificial Intelligence Biochemical Engineering Simulation and Modeling Ponce Cruz, Pedro 1971-) Molina, Arturo Studies in Computational Intelligence (CKB)1000000000238186 (DLC) (OCoLC) 1860-9503 3-319-02471-X Studies in Computational Intelligence 521