Name
Abstract | ||
|---|---|---|
The solving strategy of artificial intelligence (AI) is adopted with bottom-up design to solve its hard problems. To tackle end-to-end AI-hard problems, a highly self-adaptive control system-on-chip has been developed to self-learn its internal and external resources with the aid of sets of sensors and actuators. Inspired by biological cell learning theory, different approaches of modelling techniques have been derived together with machine learning methods to the embedded control systems so as to perform different tasks. Some experimental results have shown the developments. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ROBIO.2013.6739603 | 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO) |
Keywords | Field | DocType |
learning artificial intelligence,system on chip,cellular automata | Robot learning,Intelligent control,System on a chip,Computer science,Control engineering,Hyper-heuristic,Artificial intelligence,Control system,Artificial intelligence, situated approach,Artificial neural network,Artificial Intelligence System | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yimin Zhou | 1 | 36 | 12.53 |
| Ludovic A. Krundel | 2 | 4 | 1.44 |
| David Mulvaney | 3 | 41 | 6.50 |
| Vassilios A. Chouliaras | 4 | 75 | 10.91 |
| Guoqing Xu | 5 | 70 | 14.80 |
| Guoqiang Fu | 6 | 30 | 29.70 |