Name
Abstract | ||
|---|---|---|
Fuzzy theory was motivated by the need to create human-like solutions that allow representing vagueness and uncertainty that exist in the real-world. These capabilities have been recently further enhanced by deep learning since it allows converting complex relation between data into knowledge. In this paper, we present a novel Deep-Neuro-Fuzzy strategy for unsupervised estimation of the interaction forces in Robotic Assisted Minimally Invasive scenarios. In our approach, the capability of Neuro-Fuzzy systems for handling visual uncertainty, as well as the inherent imprecision of real physical problems, is reinforced by the advantages provided by Deep Learning methods. Experiments conducted in a realistic setting have demonstrated the superior performance of the proposed approach over existing alternatives. More precisely, our method increased the accuracy of the force estimation and compared favorably to existing state of the art approaches, offering a percentage of improvement that ranges from about 35% to 85%. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | Neuro-fuzzy,Vagueness,Computer science,Visualization,Fuzzy logic,Robotic surgery,Artificial intelligence,Deep learning,Robot,Machine learning,Interaction forces |
DocType | ISSN | Citations |
Conference | 1544-5615 | 1 |
PageRank | References | Authors |
0.36 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Angelica I. Aviles | 1 | 30 | 4.40 |
| Samar M Alsaleh | 2 | 23 | 4.42 |
| Eduard Montseny | 3 | 272 | 40.75 |
| Pilar Sobrevilla | 4 | 285 | 41.28 |
| Alicia Casals | 5 | 212 | 48.64 |