Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators
By Guilherme de Alencar Barreto, Aluizio Fausto Ribeiro Araujo, Helge J. Ritter. 2003
68 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Guilherme de Alencar Barreto
- Vincent Padois
- Camille Salaün
- Olivier Sigaud
- Alexander Rainer Tassilo Gepperth
In Computer Vision and Pattern Recognition
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Guilherme de Alencar Barreto
- Vincent Padois
- Camille Salaün
- Olivier Sigaud
- Cornelius Weber
In Control and Systems Engineering
- German Ignacio Parisi
- Luiza Mici
- Stefan Wermter
- Guilherme de Alencar Barreto
- Vincent Padois
- Camille Salaün
- Olivier Sigaud
- Alexander Rainer Tassilo Gepperth
Citations in the corpus
- An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, methodology): “This learning scheme has been successfully applied to the vector-quantized temporal associative memory (VQTAM) model [20], shown to perform well on tasks such as time series prediction and predictive control [43].”
- On-line regression algorithms for learning mechanical models of robots: A survey (2011, background): “[8] provide a good survey of the application of soms to robot identification problems.”
- Time Series Prediction with the Self-Organizing Map: A Review (2007, methodology): “This model can be understood a generalization to the temporal domain of a SOM-based associative memory technique that has been widely to learn static (memoryless) input-output mappings, specially within the domain of robotics [4].”