Improving neural networks training using experiment design approach

This project involves the use of Neural Networks (NN) for function approximation. Conventionally, the parameters of a neural network are tuned by minimizing an objective function based on a pre-determined set of training data. This training paradigm is passive in the sense that the neural network on...

Full description

Saved in:

Bibliographic Details
Main Author:	Chong, Wei Kean
Format:	Thesis
Language:	English
Published:	2005
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/3580/2/ChongWeiKeanMED2005.PDF
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.3580
record_format	uketd_dc
spelling	my-utm-ep.35802020-03-09T07:02:49Z Improving neural networks training using experiment design approach 2005-03 Chong, Wei Kean TK Electrical engineering. Electronics Nuclear engineering This project involves the use of Neural Networks (NN) for function approximation. Conventionally, the parameters of a neural network are tuned by minimizing an objective function based on a pre-determined set of training data. This training paradigm is passive in the sense that the neural network only learns from the training patterns presented to it by the environment or a teacher. It may be more useful if the neural network could 'actively' obtain the training samples itself sequentially by interacting with its environment. There are several methods of selecting training data from input space for neural networks which include D-optimal and Max-min design approaches. Consider a function approximation problem (Neural Network using Radial Basic Function structure) and limit the amount of training data, say (m) from N amount of possible data. Randomly select the m data set for conventional training algorithm. One more data (m+ 1) is entered to train the NN again. This data is selected by two methods: random and Experiment Design Approach (D-optimal and Maxmin Distance). The performances of the two approaches are then compared. It was found that the NN trained using the data obtained using experiment design approaches approximated the unknown function better than the NN that is trained when the data are selected randomly. Maxmin Distance Approach is independent of the NN model while Doptimal point is dependent on the NN model used. 2005-03 Thesis http://eprints.utm.my/id/eprint/3580/ http://eprints.utm.my/id/eprint/3580/2/ChongWeiKeanMED2005.PDF application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Chong, Wei Kean Improving neural networks training using experiment design approach
description	This project involves the use of Neural Networks (NN) for function approximation. Conventionally, the parameters of a neural network are tuned by minimizing an objective function based on a pre-determined set of training data. This training paradigm is passive in the sense that the neural network only learns from the training patterns presented to it by the environment or a teacher. It may be more useful if the neural network could 'actively' obtain the training samples itself sequentially by interacting with its environment. There are several methods of selecting training data from input space for neural networks which include D-optimal and Max-min design approaches. Consider a function approximation problem (Neural Network using Radial Basic Function structure) and limit the amount of training data, say (m) from N amount of possible data. Randomly select the m data set for conventional training algorithm. One more data (m+ 1) is entered to train the NN again. This data is selected by two methods: random and Experiment Design Approach (D-optimal and Maxmin Distance). The performances of the two approaches are then compared. It was found that the NN trained using the data obtained using experiment design approaches approximated the unknown function better than the NN that is trained when the data are selected randomly. Maxmin Distance Approach is independent of the NN model while Doptimal point is dependent on the NN model used.
format	Thesis
qualification_level	Master's degree
author	Chong, Wei Kean
author_facet	Chong, Wei Kean
author_sort	Chong, Wei Kean
title	Improving neural networks training using experiment design approach
title_short	Improving neural networks training using experiment design approach
title_full	Improving neural networks training using experiment design approach
title_fullStr	Improving neural networks training using experiment design approach
title_full_unstemmed	Improving neural networks training using experiment design approach
title_sort	improving neural networks training using experiment design approach
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2005
url	http://eprints.utm.my/id/eprint/3580/2/ChongWeiKeanMED2005.PDF
_version_	1747814455852400640

Improving neural networks training using experiment design approach

Similar Items