Name
Abstract | ||
|---|---|---|
The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A Corridor-Scene-Classifier based on spiking neural networks (SNN) for mobile robot is designed to help the mobile robot to locate correctly. In the SNN classifier, the integrate-and-fire model (IAF) spiking neuron model is used and there is lateral inhibiting in the output layer. The Winner-Take-All rule is used to modify the connecting weights between the hidden layer and the outputting layer. The experimental results show that the Corridor-Scene-Classifier is effective and it also has strong robustness. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.718 | ICNC |
Keywords | DocType | Citations |
integrate-and-fire model,SNN classifier,hidden layer,complex environment,Winner-Take-All rule,Spiking Neurons,real autonomous robot,neuron model,mobile robot,outputting layer,output layer,Mobile Robot,Corridor-Scene Classification | Conference | 5 |
PageRank | References | Authors |
0.42 | 7 | 5 |
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 |
| Xinian Wang | 5 | 5 | 0.42 |