Suaza Cano, Kevin AndrésMoofarry, Jhon FreddyCastillo García, Javier Ferney2020-10-232020-10-232020-03-03978-303042519-71865-0929http://repositorio.usc.edu.co/handle/20.500.12421/4510This paper presents implementation MLP artificial neural networks on embedded low-cost microcontrollers that can be dynamically configured on the run. The methodology starts with the training process, goes through the codification of the neural network into the microcontroller format, and finishes with the execution process of the embedded NNs. It is presented how to compute deterministically the memory space require for a certain topology, as well as the required fields to execute the neural network. The training and verification was done with Matlab and programming with a IDE Arduino compiler. The results show statistical and graphical analysis for several topologies, average execution times for various transfer function, and accuracy.enArtificial Neuronal Networks (ANN)Multilayer Perceptron (MLP)MatlabArticle