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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sasaki, H. Kubota, N. |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Syst. Design, Tokyo Metropolitan Univ. (Sasaki, H.; Kubota, N.) |
| Abstract | Recently, embodied cognition for robotics has been discussed, and various types of artificial neural networks are applied for behavior learning of mobile robots in unknown and dynamic environments. In this research, the behavioral learning based on a spiking neural network to realize high adaptability of a mobile robot is proposed. The robot learn the forward relationship from sensory inputs to motor outputs as well as the predictive relationship from motor outputs to the sensory inputs. However, the behavioral leaning capability of the robot depends strongly on the network structure. Therefore, a VEGA to acquire the network structure suitable to the changing environment is applied. Finally, the effectiveness of the proposed method through experimental results on behavioral learning for collision avoidance and target tracing behavior is discussed |
| Starting Page | 4214 |
| Ending Page | 4219 |
| File Size | 6560712 |
| Page Count | 6 |
| File Format | |
| ISBN | 8995003847 |
| DOI | 10.1109/SICE.2006.314773 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-10-18 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | The Institute of Control, Automation and Systems |
| Subject Keyword | Genetic algorithms Fuzzy neural networks Neural networks Mobile robots Robot sensing systems Fuzzy systems Fuzzy control Intelligent networks Intelligent robots Cognitive robotics robot learning SSGA VEGA SNN |
| Content Type | Text |
| Resource Type | Article |
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