On-Line Learning in an Embedded Maximum Sensibility Neural Network
Gustavo Gonzalez Sanmiguel, Luis Lauro Gonzalez, Luis Torres‐Treviño, Cesar E. Guerra · 2012
A maximum sensibility neural networks was implemented in an embedded system to make on-line learning. This neural network has advantages like easy implementation and a quick learning based on manage information in place of a gradient algorithm. The embedded maximum sensibility neural network was used to learn non linear functions on-line using potentiometers and a push button giving the function of activation and learning. The results give us a platform to apply on-line learning using neural networks.