A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features
β Scribed by V. Srinivasan; C. Eswaran; and N. Sriraam
- Publisher
- Springer US
- Year
- 2005
- Tongue
- English
- Weight
- 374 KB
- Volume
- 29
- Category
- Article
- ISSN
- 0148-5598
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract In this paper a new time domain based neural network model of a 0.18 ΞΌm RF MOSFET will be demonstrated. The model consists of only three intrinsic nonβlinear current sources, each being modelled by a neural network derived from large signal time domain voltage/current waveform measureme
In this article, a recurrent neural network (RNN) method is employed for dynamic time-domain modeling of both linear and nonlinear microwave circuits. An automated RNN modeling technique is proposed to efficiently determine the training waveform distribution and internal RNN structure during the off