Name
Abstract | ||
|---|---|---|
An elaborately designed software architecture is put forward based on fuzzy sets theory (FST), which is specialized in multiple sensor fusion and mechanism failure diagnosis. Besides, when confronted with multiple fault signals, fusion parameters can be dynamically adapted based on principles of fuzzy soft clustering so as to promote immune ability in artificially mechanical systems. The key point in this new approach lies in its power on faults detection, which requires no prior information on the state vectors of the sensors and system behavior, and no supplemental machine learning operation is required. The proposed algorithm combines principles of artificial immune system and the classical technique in fuzzy theory, which will consist of two main portions. In the first part a traditional data fuse structure is constructed, the sensor signals will be fed into it to implement the fuzzy aggregating algorithm. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/JCAI.2009.190 | JCAI |
Keywords | Field | DocType |
fuzzy set theory,multiple sensor fusion,fuzzy sets theory,pattern clustering,transit articles extraction,domestic fusion algorithm,matlab,fuzzy theory,learning (artificial intelligence),fuzzy,artificially mechanical system,data fusion,software architecture,feature extraction,fault diagnosis,mechanical system,fuzzy soft clustering,fusion parameter,fuzzy aggregating algorithm,artificial immune,mechanism failure diagnosis,immune ability,multiple fault signal,artificial immune system,mechanism system,faults detection,machine learning operation,sensor fusion,clustering algorithms,silicon,fuzzy systems,learning artificial intelligence,software design,fault detection,data mining,mechanical systems,machine learning | Fuzzy clustering,Data mining,Computer science,Fuzzy set,Artificial intelligence,Fuzzy control system,Cluster analysis,Artificial immune system,Fault detection and isolation,Fuzzy logic,Algorithm,Sensor fusion,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-3615-6 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyi Wang | 1 | 0 | 0.68 |
| Liqi Wang | 2 | 23 | 4.72 |