يعرض 1 - 15 نتائج من 15 نتيجة بحث عن '"Funciones de costo"', وقت الاستعلام: 0.69s تنقيح النتائج
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    Dissertation/ Thesis

    المؤلفون: Garzón López, Martha Milena

    المساهمون: Álvarez Pomar, Lindsay, Rojas Galeano, Sergio Andrés, Álvarez Pomar, Lindsay 0000-0002-8818-0901

    وصف الملف: pdf; application/pdf

    Relation: Antelmi, A., Cordasco, G., DAmbrosio, G., De Vinco, D., and Spagnuolo, C. (2022). Experimenting with agent-based model simulation tools. Applied Sciences, 13(1):13.; Bansal, J. C., Sharma, H., and Jadon, S. S. (2013). Artificial bee colony algorithm: A survey. International Journal of Advanced Intelligence Paradigms, 5:123–159.; Blanco, A. L., Chaparro, N., and Rojas-Galeano, S. (2019). An urban pigeon-inspired optimiser for unconstrained continuous domains. Proceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019, pages 521–526.; Blum, C., Roli, A., and Alba, E. (2005). An introduction to metaheuristic techniques. Parallel Metaheuristics: A New Class of Algorithms, 47:1.; Bortz, G. (2013). El hackaton como metodologia de produccion de bienes inofmracionales. Hipertextos, I:133–162.; Boussaïd, I., Lepagnot, J., and Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237:82–117.; Briscoe, G. and Mulligan, C. (2014). Digital Innovation: The Hackathon Phenomenon. Creativeworks London, (6):1–13.; Casadei, R. (2023). Artificial collective intelligence engineering: a survey of concepts and perspectives.; Cañon, A. L. B. (2023). Modelo de programación de la producción con cancelación de trabajos en sistemas open-shop mediante algoritmos meméticos.; Chakraborty, A. and Kar, A. K. (2017). Nature-inspired computing and optimization - theory and applications. Modeling and Optimization in Science and Technologies, 10:13.; Chopard, B. and Tomassini, M. (2018). An introduction to metaheuristics for optimization. Springer.; Dorigo, M. (1992). Optimization, learning and natural algorithms.; Dorigo, M. and Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical Computer Science, 344:243–278.; Duan, H. and Qiao, P. (2014). Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning. International Journal of Intelligent Computing and Cybernetics, 7:24–37.; Eberhart, R. C. and Kennedy, J. (1995). A new optimizer using particle swarm theory. page 3943. IEEE.; Garzón, M., Álvarez-Pomar, L., and Rojas-Galeano, S. (2022). An agent-based model of follow-the-leader search using multiple leaders. In Metaheuristics International Conference, pages 499–505. Springer.; Garzon, M. and Rojas-Galeano, S. (2019). An agent-based model of urban pigeon swarm optimisation. 2019 IEEE Latin American Conference on Computational Intelligence, LACCI 2019.; Glover, F. and Sörensen, K. (2015). Metaheuristics. Scholarpedia, 10(4):6532.; Goldberg, D. E. and Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2):95–99; Guarnizo, M. P. V. and Arévalo, L. E. B. (2022). Diseño de modelo de simulación que representa el genoma de inteligencia colectiva de malone, laubacher y dellarocas. Tecnura, 26:59–77.; He, F., Pan, Y., Lin, Q., Miao, X., and Chen, Z. (2019). Collective intelligence: A taxonomy and survey. IEEE Access, 7:170213–170225; Janssens, M., Meslec, N., and Leenders, R. T. A. (2022). Collective intelligence in teams: Contextualizing collective intelligent behavior over time. Frontiers in Psychology, 13; Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Univ. Press, Erciyes.; Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks, volume 4, pages 1942–1948. ieee; Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680; Leimeister, J. M. (2010). Collective intelligence. Business Information Systems Engineering, 2:245–248.; Lévy, P. (1997). Collective intelligence: Mankind’s emerging world in cyberspace. Perseus books; Luke, S. (2013). Essentials of Metaheuristics. Lulu, second edition. Available for free at http://cs.gmu .edu/∼sean/book/metaheuristics/.; Lykourentzou, I., Vergados, D. J., Kapetanios, E., and Loumos, V. (2011). Collective intelligence systems: Classification and modeling. volume 3, pages 217–226.; Malone, T., Laubacher, R., and Dellarocas, C. (2010). The collective intelligence genome. MIT SLOAN MANAGEMENT REVIEW, 21:20–31.; Malone, T. W. and Bernstein, M. S. (2015). Handbook of Collective Intelligence. The MIT Press; Mamykina, L., Manoim, B., Mittal, M., Hripcsak, G., and Hartmann, B. (2011). Design lessons from the fastest Q&A site in the west. Conference on Human Factors in Computing Systems - Proceedings, pages 2857–2866.; Molina, D., Poyatos, J., Ser, J. D., García, S., Hussain, A., and Herrera, F. (2020). Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations. Cognitive Computation, 12(5):897–939.; Pe-Than, E. P. P., Nolte, A., Filippova, A., Bird, C., Scallen, S., and Herbsleb, J. D. (2019). Designing Corporate Hackathons with a Purpose: The Future of Software Development. IEEE Software, 36(1):15–22; Pereda, M. and Zamarreño, J. (2015). Modelado basado en agentes: un enfoque desde la ingeniería de sistemas. Revista Iberoamericana de Automática e Informática Industrial RIAI, 12:304–312; Railsback, S. F. and Grimm, V. (2010). Agent-Based and Individual-Based Modeling: A Practical Introduction, Second Edition; Rand, W. and Rust, R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28:1–13.; Stonedahl, F. and Wilensky, U. (2008). Netlogo Particle Swarm Optimization model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, USA.; Suran, S., Pattanaik, V., Yahia, S. B., and Draheim, D. (2019). Exploratory Analysis of Collective Intelligence Projects Developed Within the EU-Horizon 2020 Framework. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11684 LNAI:285–296.; Tisue, S. and Wilensky, U. (2004). Netlogo: A simple environment for modeling complexity. In International Conference on Complex Systems. New England Complex Systems Institute; Tomar, V., Bansal, M., and Singh, P. (2024). Metaheuristic algorithms for optimization: A brief review. Engineering Proceedings, 59(1):238.; Wilensky, U. (1999). Netlogo user manual: Netlogo. center for connected learning and computer-based modeling; Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82.; Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., and Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330:686–688; http://hdl.handle.net/11349/42210

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    Academic Journal
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    Academic Journal
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    Academic Journal

    وصف الملف: application/pdf

    Relation: Universidad Nacional de Colombia Sede Medellín Facultad de Ciencias Humanas y Económicas Escuela de Economía; Escuela de Economía; Mejía Giraldo, Milena Eveyde and Oviedo Restrepo , Iván David (2006) Estimación de las funciones de costo marginal de abatimiento de material particulado para fuentes fijas en el Valle de Aburrá / Estimated marginal cost functions particulate abatement for stationary sources in the Valley of Aburrá. Ensayos de economía, 16 (29). pp. 55-81. ISSN 0121-117x; https://repositorio.unal.edu.co/handle/unal/8911; http://bdigital.unal.edu.co/5617/

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    Academic Journal

    وصف الملف: application/pdf

    Relation: http://revistas.unal.edu.co/index.php/ingeinv/article/view/14678; Universidad Nacional de Colombia Revistas electrónicas UN Ingeniería e Investigación; Ingeniería e Investigación; Ingeniería e Investigación; Vol. 26, núm. 1 (2006); 34-38 Ingeniería e Investigación; Vol. 26, núm. 1 (2006); 34-38 2248-8723 0120-5609; Giraldo Giraldo, Daniel Alonso and Santos Barbosa, Dolly and Cotrino Badillo, Carlos (2006) Estrategia de control predictivo sobre un modelo matemático de un evaporador. Ingeniería e Investigación; Vol. 26, núm. 1 (2006); 34-38 Ingeniería e Investigación; Vol. 26, núm. 1 (2006); 34-38 2248-8723 0120-5609 .; https://repositorio.unal.edu.co/handle/unal/28712; http://bdigital.unal.edu.co/18760/

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    Dissertation/ Thesis
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    Electronic Resource

    Additional Titles: Estrategia de control predictivo sobre un modelo matemático de un evaporador

    المصدر: Ingeniería e Investigación; Vol. 26 No. 1 (2006); 34-38; Ingeniería e Investigación; Vol. 26 Núm. 1 (2006); 34-38; 2248-8723; 0120-5609

    URL: https://revistas.unal.edu.co/index.php/ingeinv/article/view/14678/18673
    https://revistas.unal.edu.co/index.php/ingeinv/article/view/14678/18673
    *ref*/Brosilow, C. and Joseph, B., Techniques of Model Based Control., Upper Saddle River NJ, Prentice Hall, PTR., 2002.
    *ref*/Camacho, J. E. and Bordons, C., Model Predictive Control: Springer-Verlag, 2000.
    *ref*/Franklin, G., et al., Feedback control of dynamic systems, 4th edition, Upper Saddle River, NJ, Prentice Hall, 2002.
    *ref*/Giraldo, D., "Estrategia de control avanzado para un evaporador", Proyecto de grado presentado en la Universidad Nacional de Colombia, Bogotá, para optar al grado de Ingeniero Químico, 2002. Giraldo, D., Santos, D. y Cotrino, C. Cálculo numérico de un modelo de evaporador con recompresión mecánica de vapor, En revista Ingeniería Javeriana., No. 40, Julio 2005.
    *ref*/Norton, J.P., An introduction to identification, London, Academic Press, 1986.
    *ref*/Predictive Control Ltd., Connoisseur Technology primer, Issue 1.0., 1998.
    *ref*/Skogestad, S., et al., Multivariable feedback control: Analysis and design, Chichester, UK: John Wiley & Sons, 1996.
    *ref*/Winchester, J. A. and Marsh C., Dynamics and control of falling film evaporators with mechanical vapor recompresión, Chemical Engineering Research and Design, Vol. 77, Part A., July 1999.
    *ref*/Zhu Y, Multivariable System Identification for Process Control, Amsterdam, Pergamon, 2001.

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    Electronic Resource