Parameter Identification of Vibration Loads of Hydro Generator Using Neural Networks

2008 ◽  
Author(s):  
Lijuan Cao ◽  
Shouju Li ◽  
Zichang Shangguan
Keyword(s):  
Neural Networks ◽  
Parameter Identification ◽  
Vibration Loads

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

Parameter identification based on finite-time synchronization for Cohen–Grossberg neural networks with time-varying delays

2015 ◽  
Vol 20 (3) ◽  
pp. 348-366 ◽  
Author(s):  
Abdujelil Abdurahman ◽  
◽  
Haijun Jiang ◽  
Cheng Hu ◽  
Zhidong Teng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parameter Identification ◽  
Finite Time ◽  
Time Synchronization ◽  
Time Varying

Parameter identification via neural networks with fast convergence

2000 ◽  
Vol 51 (3-4) ◽  
pp. 157-167 ◽  
Author(s):  
N. Yadaiah ◽  
L. Sivakumar ◽  
B.L. Deekshatulu
Keyword(s):  
Neural Networks ◽  
Parameter Identification ◽  
Fast Convergence

Parameter Identification Using Neural Networks in a Vector-Controlled Induction Motor Drive

1995 ◽  
Vol 28 (19) ◽  
pp. 123-128
Author(s):  
Alejandro José Quiroga ◽  
Cristina Rivarossa ◽  
Benjamín Kuchen
Keyword(s):  
Neural Networks ◽  
Parameter Identification ◽  
Induction Motor ◽  
Motor Drive ◽  
Induction Motor Drive
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