Please use this identifier to cite or link to this item:
http://localhost:8080/xmlui/handle/20.500.12421/4510
Title: | Proposal for the Implementation of MLP Neural Networks on Arduino Platform |
Authors: | Suaza Cano, Kevin Andrés Moofarry, Jhon Freddy Castillo García, Javier Ferney |
Keywords: | Artificial Neuronal Networks (ANN) Multilayer Perceptron (MLP) Matlab |
Issue Date: | 3-Mar-2020 |
Publisher: | Springer |
Abstract: | This 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. |
URI: | http://repository.usc.edu.co/handle/20.500.12421/4510 |
ISBN: | 978-303042519-7 |
ISSN: | 1865-0929 |
Appears in Collections: | Artículos Científicos |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Proposal for the Implementation of MLP Neural Networks.png | 70.8 kB | image/png | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.