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dc.contributor.authorPonce, Hiram
dc.contributor.otherCampus Ciudad de Méxicoes
dc.creatorHIRAM EREDIN PONCE ESPINOSA;376768
dc.date.accessioned2022-02-02T16:17:36Z
dc.date.available2022-02-02T16:17:36Z
dc.date.issued2019-04-08
dc.identifier.citationPonce, H. (2019). Towards Artificial Hydrocarbon Networks: The Chemical Nature of Data-Driven Approaches," 2019 IEEE 14th International Symposium on Autonomous Decentralized System (ISADS). http://dx.doi.org/10.1109/ISADS45777.2019.9155892en
dc.identifier.isbn9781728116723
dc.identifier.urihttps://hdl.handle.net/20.500.12552/5896
dc.identifier.urihttps://doi.org/10.1109/ISADS45777.2019.9155892
dc.description.abstractInspiration in nature has been widely explored, from macro to micro-scale. Natural phenomena mainly considers adaptability, optimization, robustness, organization, among other properties, to deal with complexity. When looking into chemical phenomena, stability and organization are two properties that emerge. Recently, artificial hydrocarbon networks (AHN), a supervised learning method inspired in the inner structures and mechanisms of chemical compounds, have been proposed as a data-driven approach in artificial intelligence. AHN have been successfully applied in data-driven approaches, such as: regression and classification models, control systems, signal processing, and robotics. To do so, molecules-the basic units of information in AHN-play an important role in the stability, organization and interpretability of this method. Until now, building the architecture of AHN has been treated as a whole entity; but distributed computing mechanisms, as well as the exploitation of hierarchical organization of molecules, can enhance the performance of AHN. Thus, this paper aims to discuss challenges and trends of artificial hydrocarbon networks as a data-driven method, with emphasis on packaging, distributed computing and hierarchical properties. Throughout this work, it presents a description of the main insights of AHN and the proposed distributed and hierarchical mechanisms in molecules. Potential applications and future trends on AHN are also discussed. © 2019 IEEE.en
dc.description.tableofcontentsI. Introduction -- II. Inspiration for organic compounds -- A. Stability -- B. Organization -- C. Multi-functionality -- III. Insights on artificial hydrocarbon networks -- A. Packaging Information -- B. Description of the AHN-Algorithm -- C. Stability Analysis -- D. Data-Driven Performed by AHN -- IV. Distributed molecules and hierarchical chemical architectures -- A. Distributed Molecules -- B. Hierarchical Topologies -- V. Trends on artificial hycrocarbon networks -- Conclusionsen
dc.language.isoengen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en
dc.relation.ispartofREPOSITORIO SCRIPTAes
dc.relation.ispartofOPENAIREes
dc.rightsAcceso Restringidoes
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceProceedings - 2019 IEEE 14th International Symposium on Autonomous Decentralized Systemsen
dc.subjectAgentsen
dc.subjectArtificial hydrocarbon networksen
dc.subjectArtificial organic networksen
dc.subjectDistributed systemsen
dc.subjectMachine learningen
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes
dc.subject.classificationIngenieríaes
dc.titleTowards artificial hydrocarbon networks : The chemical nature of data-driven approachesen
dc.typeContribución a congresoes
dcterms.audienceInvestigadoreses
dcterms.audienceMaestroses
dcterms.audienceEstudianteses
dcterms.bibliographicCitationW.H. Brown, C.S. Foote, B.L. Iverson and E.V. Anslyn, Organic chemistry, Cengage Learning, 2011.en
dcterms.bibliographicCitationJason Brownlee, Clever algorithms: Nature-inspired programming recipes, Jason Brownlee, 2011.en
dcterms.bibliographicCitationF.A. Carey and R.J. Sundberg, Advanced organic chemistry: part A: structure and mechanisms, Springer, 2007.en
dcterms.bibliographicCitationUjjwal Das Gupta, Vinay Menon and Uday Babbar, "Detecting the number of clusters during expectation-maximization clustering using information criterion", Second International Conference on Machine Learning and Computing, pp. 169-173, 2010.en
dcterms.bibliographicCitationD.R. Klein, Organic chemistry, Wiley, 2011.en
dcterms.bibliographicCitationR.J. Martín-Palma and A. Lakhtakia, "Engineered biomimicry for harvesting solar energy: a bird’s eye view", International Journal of Smart Nano Materials, vol. 4, no. 2, pp. 83-90, 2013.en
dcterms.bibliographicCitationH. Ponce, L. Miralles-Pechuan and L. Martinez-Villasenor, "Artificial hydrocarbon networks for online sales prediction" in Advances in Artificial Intelligence and Its Applications volume 9414 of Lecture Notes in Computer Science, Springer, pp. 498-508, 2015.en
dcterms.bibliographicCitationH. Ponce, P. Ponce and A. Molina, "Adaptive noise filtering based on artificial hydrocarbon networks: An application to audio signals", Expert Systems With Applications, vol. 41, no. 14, pp. 6512-6523, 2014.en
dcterms.bibliographicCitationH. Ponce, P. Ponce and A. Molina, Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks volume 521 of Studies in Computational Intelligence, Springer, 2014.en
dcterms.bibliographicCitationH. Ponce, P. Ponce and A. Molina, "The development of an artificial organic networks toolkit for LabVIEW", Journal of Computational Chemistry, vol. 36, no. 7, pp. 478-492, 2015.en
dcterms.bibliographicCitationHiram Ponce and Mario Acevedo, "Design and equilibrium control of a force-balanced one-leg mechanism" in Advances in Soft Computing Lecture Notes in Computer Science, Springer, pp. 1-15, 2018.en
dcterms.bibliographicCitationHiram Ponce and Roberto Ayala-Solares, The Power of Natural Inspiration in Control Systems volume 40 of Studies in Systems Decision and Control, Springer, vol. chapter 1, pp. 1-10, 2016.en
dcterms.bibliographicCitationHiram Ponce, Guillermo González-Mora and Lourdes Martínez-Villase nor, "A reinforcement learning method for continuous domains using artificial hydrocarbon networks", 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-6, 2018.en
dcterms.bibliographicCitationHiram Ponce and Sebastián Gutiérrez, "An indoor predicting climate conditions approach using internet-of-things and artificial hydrocarbon networks", Measurement, 2018.en
dcterms.bibliographicCitationHiram Ponce, Luis Miralles-Pechuán and Lourdes Martínez-Villase nor, "A flexible approach for human activity recognition using artificial hydrocarbon networks", Sensors, vol. 16, no. 11, pp. 1715, 2016.en
dcterms.bibliographicCitationHiram Ponce, Ernesto Moya-Albor and Jorge Brieva, "A novel artificial organic control system for mobile robot navigation in assisted living using vision and neural based strategies", Computational Intelligence and Neuroscience, vol. 2018, no. 4189150, 2018.en
dcterms.bibliographicCitationHiram Ponce and Lourdes Martínez-Villase nor, "Interpretability of artificial hydrocarbon networks for breast cancer classification", International Joint Conference on Neural Networks, pp. 3535-3542, 2017.en
dcterms.bibliographicCitationHiram Ponce and Lourdes Martínez-Villase nor, "Versatility of artificial hydrocarbon networks for supervised learning" in Advances in Soft Computing Lecture Notes in Computer Science, Springer, pp. 1-14, 2018.en
dcterms.bibliographicCitationHiram Ponce, Lourdes Martínez-Villase nor and Luis Miralles-Pechuán, "A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks", Sensors, vol. 16, no. 7, pp. 1033, 2016.en
dcterms.bibliographicCitationHiram Ponce, Pedro Ponce and Arturo Molina, "Artificial hydrocarbon networks fuzzy inference system", Mathematical Problems in Engineering, vol. 2013, 2013.en
dcterms.bibliographicCitationPedro Ponce, Hiram Ponce and Arturo Molina, "Doubly fed induction generator (DFIG) wind turbine controlled by artificial organic networks", Soft Computing, vol. 22, no. 9, pp. 2867-2879, 2018.en
dcterms.bibliographicCitationG. Rozenberg, T. Bck and J.N. Kok, Handbook of natural computing, Springer, 2011.en
dcterms.bibliographicCitationPinar Tufekci, "Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods", International Journal of Electrical Power & Energy Systems, vol. 60, no. 9, pp. 126-140, 2014.en
dcterms.bibliographicCitationHongtao Xue, Man Wang, Zhongxing Li and Peng Chen, "Fault feature extraction based on artificial hydrocarbon network for sealed deep groove ball bearings of in-wheel motor", 2017 Prognostics and System Health Management Conference (PHM-Harbin), pp. 1-5, 2017.en
dcterms.bibliographicCitationI Cheng Yen, "Modeling of strength of high performance concrete using artificial neural networks", Cement and Concrete Research, vol. 28, no. 12, pp. 1797-1808, 1998.en
dc.description.versionVersión del editores
dc.identifier.doihttps://doi.org/10.1109/ISADS45777.2019.9155892


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