Elliot Waves Predicting for Stock Marketing Using Euclidean Based Normalization Method Merged with Artificial Neural Network

Author(s):  
Bayram Akdemir ◽  
Lingwen Yu
2011 ◽  
Vol 84-85 ◽  
pp. 442-446
Author(s):  
Bao Yu Xu ◽  
Xiao Zhuo Xu ◽  
Yi Lun Liu ◽  
Xu Dong Wang

Based on wavelet transform and artificial neural network, a novel method which takes advantage of both the multi-resolution decomposition of wavelet transform and the classification characteristics of artificial neural network is proposed for fault detection of permanent magnet linear synchronous motor (PMLSM). According to the characteristic of unhealthy PMLSM, the wavelet transform is carried out to decompose and reconstruct winding current signal. Then the energy of each frequency band as faulty features can be detected through spectrum analysis of wavelet coefficients about each frequency band. With normalization method, the feature vectors are constructed from relative energy for energy of each frequency band. The proposed method is applied to the fault detection of PMLSM, and the result of simulation proved that the wavelet neural network can effectively detect different conditions of PMLSM.


2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman

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