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| Content Provider | Springer Nature Link |
|---|---|
| Author | Gong, Tao Cai, Zi xing He, Han gen |
| Copyright Year | 2006 |
| Abstract | An artificial immune system was modelled with self/non-self selection to overcome abnormity in a mobile robot demo. The immune modelling includes the innate immune modelling and the adaptive immune modelling. The self/non-self selection includes detection and recognition, and the self/non-self detection is based on the normal model of the demo. After the detection, the non-self recognition is based on learning unknown non-self for the adaptive immunization. The learning was designed on the neural network or on the learning mechanism from examples. The last step is elimination of all the non-self and failover of the demo. The immunization of the mobile robot demo is programmed with Java to test effectiveness of the approach. Some worms infected the mobile robot demo, and caused the abnormity. The results of the immunization simulations show that the immune program can detect 100% worms, recognize all known Worms and most unknown worms, and eliminate the worms. Moreover, the damaged files of the mobile robot demo can all be repaired through the normal model and immunization. Therefore, the immune modelling of the mobile robot demo is effective and programmable in some anti-worms and abnormity detection applications. |
| Starting Page | 694 |
| Ending Page | 698 |
| Page Count | 5 |
| File Format | |
| ISSN | 10059784 |
| Journal | Journal of Central South University of Technology |
| Volume Number | 13 |
| Issue Number | 6 |
| Language | English |
| Publisher | Central South University of Technology |
| Publisher Date | 2006-01-01 |
| Publisher Place | Changsha |
| Access Restriction | Subscribed |
| Subject Keyword | artificial immune system normal model mobile robot worms Metallic Materials Engineering |
| Content Type | Text |
| Resource Type | Article |
| Subject | Mechanics of Materials Materials Science Mechanical Engineering |
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