Name
Title | ||
|---|---|---|
Tension Identification of Multi-motor Synchronous System Based on Artificial Neural Network |
Abstract | ||
|---|---|---|
Sensorlesstension control of multi-motor synchronous system with closed tension loop is required in many fields. How to identify the knowledge of instantaneous magnitude of tension is key. In this paper the tension identification is managed on the base of stator currents and its previous values with neural network. According to the fundamental state equations of multi-motor system for tension control, the novel method of tension identification using neural network is presented .A multi-layer feed-forward neural network (MFNN) is trained by Back Propagation Levenberger-Marquardt's method. Simulation and experiment results show that the system with tension identification via a neural network has better performance, and it can be used in many application fields. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72383-7_76 | ISNN (1) |
Keywords | Field | DocType |
closed tension loop,novel method,multi-motor synchronous system,neural network,sensorlesstension control,multi-layer feed-forward neural network,artificial neural network,multi-motor system,tension identification,tension control,propagation levenberger-marquardt,back propagation,levenberg marquardt,feed forward neural network,motor system | Induction motor,Physical neural network,Computer science,Stochastic neural network,Probabilistic neural network,Time delay neural network,Artificial intelligence,Stator,Backpropagation,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
4491 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Guo | 1 | 77 | 15.02 |
| Jianbing Wu | 2 | 0 | 0.34 |
| Yue Shen | 3 | 0 | 1.01 |
| Hongping Jia | 4 | 0 | 0.68 |
| Hua-Wei Zhou | 5 | 32 | 9.04 |