Name
Title | ||
|---|---|---|
Improved mobile robot's Corridor-Scene Classifier based on probabilistic Spiking Neuron Model |
Abstract | ||
|---|---|---|
The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A improved Corridor-Scene-Classifier based on probabilistic Spiking Neuron Model(pSNM) for mobile robot is designed. In the SNN classifier, the model pSNM is used. As network's training, Thorpe's learning rule is used. The experimental results show that the improved Classifier is more effective and it also has stronger robustness than the previous classifier based on Integrated-and-Fire (IAF) spiking neuron model for the structural corridor-scene. It also has better robustness than the traditional kernel-pca and the BP Corridor-Scene-classifier. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/COGINF.2011.6016164 | IEEE ICCI*CC |
Keywords | DocType | Volume |
learning (artificial intelligence),bp corridor-scene-classifier,pattern classification,probabilistic spiking neuron model,mobile robots,integrated-and-fire spiking neuron model,autonomous robot,backpropagation,thorpe's learning rule,snn classifier,natural scenes,mobile robot,neural nets,robot vision,probability,spiking neural network,learning artificial intelligence | Conference | null |
Issue | ISBN | Citations |
null | 978-1-4577-1695-9 | 2 |
PageRank | References | Authors |
0.37 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuqing Wang | 1 | 23 | 3.98 |
| Zeng-Guang Hou | 2 | 2293 | 167.18 |
| Min Tan | 3 | 2342 | 201.12 |
| Yongji Wang | 4 | 606 | 75.34 |
| Siyao Fu | 5 | 103 | 14.95 |
| Lihui Chen | 6 | 28 | 3.76 |