Novel Application of Linear Scaling to Improve Accuracy of Optimized Artificial Neural Network Using Levenberg-Marquardt Algorithm in Prediction of Daily Nitrogen Oxide for Health Management

2020 ◽  
pp. 665-688
Author(s):  
Vibha Yadav ◽  
Satyendra Nath
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nitrogen Oxide ◽  
Health Management ◽  
Linear Scaling ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Improve Accuracy ◽  
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.

scholarly journals Controlling Electronic Devices with Brain Rhythms/Electrical Activity Using Artificial Neural Network (ANN)

2019 ◽  
Vol 6 (2) ◽  
pp. 46 ◽  
Author(s):  
Yar Muhammad ◽  
Daniil Vaino
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Brain Activity ◽  
Electronic Devices ◽  
Testing Dataset ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research study was to explore the possibility to develop a brain-computer interface (BCI). The main objective was that the BCI should be able to recognize brain activity. BCI is an emerging technology which focuses on communication between software and hardware and permitting the use of brain activity to control electronic devices, such as wheelchairs, computers and robots. The interface was developed, and consists of EEG Bitronics, Arduino and a computer; moreover, two versions of the BCIANNET software were developed to be used with this hardware. This BCI used artificial neural network (ANN) as a main processing method, with the Butterworth filter used as the data pre-processing algorithm for ANN. Twelve subjects were measured to collect the datasets. Tasks were given to subjects to stimulate brain activity. The purpose of the experiments was to test and confirm the performance of the developed software. The aim of the software was to separate important rhythms such as alpha, beta, gamma and delta from other EEG signals. As a result, this study showed that the Levenberg–Marquardt algorithm is the best compared with the backpropagation, resilient backpropagation, and error correction algorithms. The final developed version of the software is an effective tool for research in the field of BCI. The study showed that using the Levenberg–Marquardt learning algorithm gave an accuracy of prediction around 60% on the testing dataset.

Comparison of Various Methods for Simultaneous Retrieval of Surface Emissivities and Gas Properties in Gray Participating Media

2006 ◽  
Vol 128 (8) ◽  
pp. 829-837 ◽  
Author(s):  
M. Deiveegan ◽  
C. Balaji ◽  
S. P. Venkateshan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Fluxes ◽  
Radiative Properties ◽  
Simultaneous Estimation ◽  
Good Precision ◽  
Participating Media ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Artificial Neural

Abstract An inverse radiation analysis for simultaneous estimation of the radiative properties and the surface emissivities for a participating medium in between infinitely long parallel planes, from the knowledge of the measured temperatures and heat fluxes at the boundaries, is presented. The differential discrete ordinate method is employed to solve the radiative transfer equation. The present analysis considers three types of simple scattering phase functions. The inverse problem is solved through minimization of a performance function, which is expressed by the sum of squares of residuals between calculated and observed temperatures and heat fluxes at the boundaries. To check the performance and accuracy in retrieval, a comparison is presented between four retrieval methods, viz. Levenberg-Marquardt algorithm, genetic algorithm, artificial neural network, and the Bayesian algorithm. The results of the present analyses indicate that good precision in retrieval could be achieved by using only temperatures and heat fluxes at the boundaries. The study shows that the radiative properties of medium and surface emissivities can be retrieved even with noisy data using Bayesian retrieval algorithm and artificial neural network. Also, the results demonstrate that genetic algorithms are not efficient but are quite robust. Additionally, it is observed that an increase in the error in measurements significantly deteriorates the retrieval using the Levenberg-Marquardt algorithm.

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