Name
Abstract | ||
|---|---|---|
A computational approach to direct and generalizedinverse model acquisition is presented. The approach isbased on a proposed method to direct model acquisitionfrom partial information. The method decomposes anhyper-space function in one variable functions, simplifyingthe learning problem. The acquired direct model is thenimplemented in a tree-like structure that can be used in theinverse sense without additional learning effort.Our approach is able to acquire complete models in... |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/ROBOT.1995.525493 | PROCEEDINGS OF 1995 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-3 |
Keywords | Field | DocType |
data acquisition,robots,inverse problems,learning artificial intelligence,shape,machine learning,function decomposition,inverse modeling,functional decomposition,multidimensional systems,robotics,generalized inverse,phase space,interpolation | Inverse,Control theory,Data acquisition,Functional decomposition,Generalized inverse,Inverse problem,Artificial intelligence,Robot,Robotics,Mathematics,Multidimensional systems | Conference |
ISSN | Citations | PageRank |
1050-4729 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Remis Balaniuk | 1 | 94 | 15.46 |
| Emmanuel Mazer | 2 | 272 | 58.70 |
| Pierre Bessière | 3 | 425 | 86.40 |