Fault Classification in a Transmission Line Using Levenberg–Marquardt Algorithm Based Artificial Neural Network

2019 ◽  
pp. 119-135 ◽  
Author(s):  
Harkamaldeep Kaur ◽  
Manbir Kaur
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transmission Line ◽  
Fault Classification ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

A Comparison of Artificial Neural Network Learning Algorithms for Vibration-Based Damage Detection

2010 ◽  
Vol 163-167 ◽  
pp. 2756-2760 ◽  
Author(s):  
Goh Lyn Dee ◽  
Norhisham Bakhary ◽  
Azlan Abdul Rahman ◽  
Baderul Hisham Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Conjugate Gradient ◽  
Learning Algorithms ◽  
Mode Shapes ◽  
Neural Network Learning ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance.

Artificial neural network modelling of adsorbent bed in a solar adsorption refrigeration system

2012 ◽  
Vol 227 (2) ◽  
pp. 346-358 ◽  
Author(s):  
V Baiju ◽  
C Muraleedharan
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Refrigeration System ◽  
Back Propagation Algorithm ◽  
Adsorption Refrigeration ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

This article analyses the adsorbent bed in an adsorption refrigeration system. After establishing the similarity to the compression process in a vapour compression system, thermodynamic analysis of the adsorbent bed in vapour adsorption system is carried out for evaluating the performance index, exergy destruction, uptake efficiency and exergetic efficiency of the adsorbent bed in a typical solar adsorption refrigeration system. This article also presents isothermal and isobaric modelling of methanol on highly porous activated carbon. The experimental data have been fitted with Dubinin–Astakhov and Dubinin–Radushkevitch equations. The isosteric heat of adsorption is also extracted from the present experimental data. The use of artificial neural network model is proposed to predict the performance of the adsorbent bed used. The back propagation algorithm with three different variants namely scaled conjugate gradient, Pola–Ribiere conjugate gradient and Levenberg–Marquardt and logistic sigmoid transfer function are used, so that the best approach could be found. After training, it is found that Levenberg–Marquardt algorithm with 14 neurons is the most suitable for modelling, the adsorbent bed in a solar adsorption refrigeration system. The artificial neural network predictions of performance parameters agrees well with experimental values with correlation coefficient ( R2) values close to 1 and maximum percentage of error less than 5%. The root mean square and covariance values are also found to be within the acceptable limits.

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