Welcome to the official Artificial Organic Networks website.

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


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

  1. Ponce, P., Ponce, H., Molina, A., (2017), A Doubly Fed Induction Generator (DFIG) Wind Turbine Controlled by Artificial Organic Networks, Soft Computing, DOI: 10.1007/s00500-017-2537-3.
  2. Ponce, H., Miralles-Pechuán, L., Martínez-Villaseñor, L. (2016), A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks, Sensors, 16(11): 1715.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Ponce, H., Ponce, P., Molina, A. (2013), Método de Aprendizaje Automático Basado en Compuestos Orgánicos (A Machine Learning Mehthod Inspired on Organic Compounds), Komputer Sapiens, V(III): 28 – 33.
  11. Ponce, H., Ponce, P., Molina, A. (2013), Artificial Hydrocarbon Networks Fuzzy Inference System. Mathematical Problems in Engineering, 2013, ID 531031, 13 pp.
  12. 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.

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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