Name
Abstract | ||
|---|---|---|
In view of the security and stability of the large system today, the intelligent fault diagnosis becomes particularly importan. This paper proposes to apply the neural network knowledge to the fault diagnosis system, and takes the digital control module of some system for example. The paper also introduces the basic principle of neural network knowledge and the training algorithm of BP. Finally, according with the specific characteristics of digital control module, to give the structure of the neural network diagnosis system, and build the BP network to study, train and emulate, the results prove that the intelligent fault diagnosis system is feasible, and has strong practical value. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CIS.2011.98 | CIS |
Keywords | Field | DocType |
neural network,neural network diagnosis system,fault diagnosis system,system security,neurocontrollers,bp network,basic principle,intelligence,intelligent fault diagnosis,specific characteristic,intelligent fault diagnosis system,system stability,backpropagation,large system,bp algorithm,fault diagnosis,stability,digital control,bp training algorithm,digital control module,neural network knowledge,security,computational intelligence | Computational intelligence,Computer science,Time delay neural network,Artificial intelligence,Backpropagation,Artificial neural network,Digital control,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4577-2008-6 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Chang | 1 | 20 | 9.20 |
| Hongwei Chen | 2 | 21 | 6.27 |
| Yingying Wang | 3 | 21 | 11.64 |
| Yueou Ren | 4 | 2 | 1.13 |
| Hao Kang | 5 | 0 | 0.34 |