Deep Learning application to model learning in cognitive robotics
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| Autores: | , , , |
|---|---|
| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2020 |
| Descripción: | The kind of training used for an artificial neural network will depend on factors such as: available data, training time, hardware resources, etc. The trainings can be online and offline. In the current article we experimented with online trainings on a robot whose main characteristic is the usage of a Cognitive Darwinist Mechanism to survive. The robot learns in real-time. It has deep artificial neural networks to predict actions, it’s trained using the least amount of storage and the training time has to be as fast as possible; keeping high confidence in the artificial neural network. The experimental trainings are: Online Deep Learning, Online Deep Learning with memory and Online Mini-Batch Deep Learning with memory. |
| País: | RepositorioTEC |
| Institución: | Instituto Tecnológico de Costa Rica |
| Repositorio: | RepositorioTEC |
| Lenguaje: | Español |
| OAI Identifier: | oai:repositoriotec.tec.ac.cr:2238/12088 |
| Acceso en línea: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/5171 https://hdl.handle.net/2238/12088 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial neural network batch training mini-batch training deep learning cognitive robotics machine learning stochastic optimizer Baxter robot Red neuronal artificial entrenamiento con batch entrenamiento con mini-batch aprendizaje profundo robótica cognitiva aprendizaje automático optimizadores estocásticos robot Baxter |