Name
Abstract | ||
|---|---|---|
Intelligent behaviors generally mean actions showing their objectives and proper sequences. For robot, to complete a given task properly, an intelligent computational model is necessary. Recurrent Neural Network (RNN) is one of the plausible computational models because the RNN can learn from previous experiences and memorize those experiences represented by inner state within the RNN. There are other computational models like hidden Markov model (HMM) and Support Vector Machine, but they are absent of continuity and inner state. In this paper, we tested several intelligent capabilities of the RNN, especially for memorization and generalization even under kidnapped situations, by simulating mobile robot in the experiments. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-31293-4_2 | ROBOT INTELLIGENCE TECHNOLOGY AND APPLICATIONS 4 |
Field | DocType | Volume |
Computer vision,Computer science,Support vector machine,Recurrent neural network,Computational model,Artificial intelligence,Hidden Markov model,Robot,Memorization,Trajectory,Mobile robot | Conference | 447 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| You-Min Lee | 1 | 0 | 0.68 |
| Jong-Hwan Kim | 2 | 8 | 6.01 |