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Welcome to the official Artificial Organic Networks website.

AHN at ICML 2019, please click here!

Here, you will find a description of this learning technique, useful fundamentals about the Artificial Hydrocarbon Networks algorithm and resources.

Artificial Organic Networks - Learn about
Artificial Hydrocarbon Networks - Learn about

Publications

The list of main publications about Artificial Organic Networks and Artificial Hydrocarbon Networks is provided below:

Journal Publications

  1. Gutiérrez, S., Ponce, H. (2019), An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions. Sensors, vol. 19(4): 854.
  2. Ponce, H., Gutiérrez, S. (2019), An indoor predicting climate conditions approach using Internet-of-Things and artificial hydrocarbon networks. Measurement, vol. 135, 170 -- 179.
  3. Ponce, H., Moya-Albor, E., Brieva, J. (2018), 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, ID: 4189150.
  4. Ponce, P., Ponce, H., Molina, A. (2018), Doubly Fed Induction Generator (DFIG) Wind Turbine Controlled by Artificial Organic Networks. Soft Computing, vol. 22(9), pp. 2867 – 2879.
  5. Ponce, H., Miralles-Pechuán, L., Martínez-Villaseñor, L. (2016), A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks, Sensors, 16(11): 1715.
  6. Ponce, H., Martínez-Villaseñor, L., Miralles-Pechuán, L. (2016), A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks. Sensors, 16(7): 1033.
  7. Ponce, H., Ponce, P., Bastida, H., Molina, A. (2015), A Novel Robust Liquid Level Controller for Coupled-Tanks Systems Using Artificial Hydrocarbon Networks. Expert Systems With Applications, 42(22): 8858 – 8867.
  8. Ponce, H., Ponce, P., Molina, A. (2015), The Development of an Artificial Organic Networks Toolkit for LabVIEW. Journal of Computational Chemistry, 36(7): 478 – 492.
  9. Miralles, L., Ponce, H. (2015), Predicción del CTR de los Anuncios de Internet Usando Redes Orgánicas Artificiales (CTR Prediction of Online Advertising Using Artificial Organic Networks), Journal of Research in Computing Science, 93(2015): 23 – 32.
  10. Ponce, H., Ibarra, L., Ponce, P., Molina, A. (2014), A Novel Artificial Hydrocarbon Networks Based Space Vector Pulse Width Modulation Controller for Induction Motors. American Journal of Applied Sciences, 11(5): 789 – 810.
  11. Molina, A., Ponce, H., Ponce, P., Tello, G., Ramírez, M. (2014), Artificial Hydrocarbon Networks Fuzzy Inference Systems for CNC Machines Position Controller. International Journal of Advanced Manufacturing Technology, 72(9-12): 1465 – 1479.
  12. Ponce, H., Ponce, P., Molina, A. (2014), Adaptive Noise Filtering Based on Artificial Hydrocarbon Networks: An Application to Audio Signals. Expert Systems With Applications, 41(14): 6512 – 6523.
  13. Ponce, H., Ponce, P., Molina, A. (2013), Método de Aprendizaje Automático Basado en Compuestos Orgánicos (A Machine Learning Method Inspired on Organic Compounds), Komputer Sapiens, V(III): 28 – 33.
  14. Ponce, H., Ponce, P., Molina, A. (2013), Artificial Hydrocarbon Networks Fuzzy Inference System. Mathematical Problems in Engineering, 2013, ID 531031, 13 pp.
  15. Ponce, H., Ponce, P. (2012), Artificial Hydrocarbon Networks: A New Algorithm Bio-Inspired on Organic Chemistry. International Journal of Artificial Intelligence and Computational Research, 4(1): 39 – 51.

Publications in Conference Proceedings

  1. Ponce, H., Martínez-Villaseñor, L. (2018), Versatility of artificial hydrocarbon networks for supervised learning, MICAI 2017: Advances in Soft Computing, 3 – 16.
  2. Ponce, H., Acevedo, M. (2018), Design and equilibrium control of a force-balanced one-leg mechanism, MICAI 2018: Advances in Soft Computing, 276 – 290.
  3. Ponce, H., González-Mora, G., Miralles-Pechuán, L., Martínez-Villaseñor, L. (2018), Human activity recognition on mobile devices using artificial hydrocarbon networks, MICAI 2017: Advances in Soft Computing, 17 – 29.
  4. Ponce, H., González-Mora, G., Martínez-Villaseñor, L. (2018), A reinforcement learning method for continuous domains using artificial hydrocarbon networks, Proceedings of the International Joint Conference on Neural Networks, 1 – 5.
  5. Ponce, H., Gutiérrez, S., Montoya, A., (2017), Predicting Climate Conditions Using Internet-of-Things and Artificial Hydrocarbon Networks, 7th IMEKO TC19 Symposium on Environmental Instrumentation and Measurements, pp. 62 – 66.
  6. Ponce, H., Martínez-Villaseñor, L., (2017), Interpretability of Artificial Hydrocarbon Networks for Breast Cancer Classification, IEEE 2017 International Joint Conference on Neural Networks, pp. 3535 – 3542.
  7. Ponce, H. (2017), A Novel Artificial Hydrocarbon Networks Based Value Function Approximation in Hierarchical Reinforcement Learning, 15th Mexican International Conference on Artificial Intelligence, Lecture Notes in Computer Science, vol. 10062, pp. 211 – 225.
  8. Ponce, H., Miralles, L., Martínez, L. (2015), Artificial Hydrocarbon Networks for Online Sales Prediction, in 14th Mexican International Conference on Artificial Intelligence, Lecture Notes in Computer Science, vol. 9414, pp. 498 – 508.
  9. Ponce, H., Martínez, L., Miralles, L., (2015), Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition, in Ubiquitous Computing and Ambient Intelligence: Sensing, Processing and Using Environmental Information, Lecture Notes in Computer Science, vol. 9454, pp. 150 – 161.
  10. Ponce, H., (2014), Bio-Inspired Training Algorithms for Artificial Hydrocarbon Networks: A Comparative Study. IEEE Proceedings on 13th Mexican International Conference on Artificial Intelligence, pp. 162 – 166.
  11. Ponce, H., Ponce, P., Molina, A. (2013), A New Training Algorithm for Artificial Hydrocarbon Networks Using an Energy Model of Covalent Bonds. 7th IFAC Conference on Manufacturing Modelling, Management, and Control, vol. 7(1), pp. 602 – 608.
  12. Ponce, H., Ponce, P., Molina, A. (2012), A Novel Adaptive Filtering for Audio Signals Using Artificial Hydrocarbon Networks. 9th International Conference on Electrical Engineering, Computing Science and Automatic Control CCE, pp. 277 – 282.
  13. Ponce, H., Ponce, P. (2011), Artificial Organic Networks. Conference on Electronics, Robotics, and Automotive Mechanics IEEE CERMA, pp. 29 – 34.
  14. Ponce, H., Ponce, P. (2011), Artificial Hydrocarbon Networks. IX Congreso Internacional sobre Innovación y Desarrollo Tecnológico CIINDET, pp. 614 – 618.

Books and Chapter Books

  1. Ponce, H., Ponce, P., Molina, A. (2014), Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks. Studies in Computational Intelligence, Vol. 521, Springer: Switzerland.
  2. Ponce, H., Moya-Albor, E., Brieva, J. (2016), A Novel Artificial Organic Controller With Hermite Optical Flow Feedback for Mobile Robot Navigation, in Artificial Intelligence in Power Electronics, Ponce, P., et. al. (Eds.). InTech: Croatia, pp. 145 – 169.

The "functional molecule" logo represents how molecular units in the method "catch" (package) data and model the latter as function-like behaviors, and how they are related among them in finite degrees of freedom.

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