Online learning algorithms for nonlinear regression of time - varying systems
In industry, there often exists a need from manufacturers for a model that accurately predicts machine yield and allows further fine-tuning of input settings to improve quality without physical testing; saving on cost and time. And this can be achieved by machine learning, since machine learning is...
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sg-ntu-dr.10356-639102023-07-07T17:23:59Z Online learning algorithms for nonlinear regression of time - varying systems Ye, Yi Yang Hu Jinwen Er Meng Joo Song Qing School of Electrical and Electronic Engineering A*STAR SIMTech DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering In industry, there often exists a need from manufacturers for a model that accurately predicts machine yield and allows further fine-tuning of input settings to improve quality without physical testing; saving on cost and time. And this can be achieved by machine learning, since machine learning is to build prediction model based on historical data collected. This project seeks to investigate the optimal method for modelling an Inkjet Page Yield Testing System using real production data, by analyzing and evaluating the models in terms of training speed and accuracy. In this project, three typical machine learning methods are chosen, 2 of them online--Extreme Learning Machine, Quantized Kernel Least Mean Square, 1 of them off-line--Gaussian Process Regression, to do the simulation. Bachelor of Engineering 2015-05-20T03:42:27Z 2015-05-20T03:42:27Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/63910 en Nanyang Technological University 73 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Ye, Yi Yang Online learning algorithms for nonlinear regression of time - varying systems |
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In industry, there often exists a need from manufacturers for a model that accurately predicts machine yield and allows further fine-tuning of input settings to improve quality without physical testing; saving on cost and time. And this can be achieved by machine learning, since machine learning is to build prediction model based on historical data collected. This project seeks to investigate the optimal method for modelling an Inkjet Page Yield Testing System using real production data, by analyzing and evaluating the models in terms of training speed and accuracy. In this project, three typical machine learning methods are chosen, 2 of them online--Extreme Learning Machine, Quantized Kernel Least Mean Square, 1 of them off-line--Gaussian Process Regression, to do the simulation. |
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Hu Jinwen |
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Hu Jinwen Ye, Yi Yang |
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Final Year Project |
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Ye, Yi Yang |
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Ye, Yi Yang |
title |
Online learning algorithms for nonlinear regression of time - varying systems |
title_short |
Online learning algorithms for nonlinear regression of time - varying systems |
title_full |
Online learning algorithms for nonlinear regression of time - varying systems |
title_fullStr |
Online learning algorithms for nonlinear regression of time - varying systems |
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Online learning algorithms for nonlinear regression of time - varying systems |
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online learning algorithms for nonlinear regression of time - varying systems |
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2015 |
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http://hdl.handle.net/10356/63910 |
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1772826087466729472 |