Quantification of human driving skill for human adaptive mechatronics applications by using neural network system

Human Adaptive Mechatronic (HAM) is a two way relationship between human and machine. In HAM, not only human need to adapt the characteristics of machine but the machine also has to learn on human characteristic. HAM is an enhance system for Human Machine System (HMS) which consists only one way rel...

Full description

Saved in:

Bibliographic Details
Main Author:	Mazni, Mazleenda
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/43975/1/MazleedaMazniMFKE2014.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.43975
record_format	uketd_dc
spelling	my-utm-ep.439752017-09-10T08:04:45Z Quantification of human driving skill for human adaptive mechatronics applications by using neural network system 2014-01 Mazni, Mazleenda TK Electrical engineering. Electronics Nuclear engineering Human Adaptive Mechatronic (HAM) is a two way relationship between human and machine. In HAM, not only human need to adapt the characteristics of machine but the machine also has to learn on human characteristic. HAM is an enhance system for Human Machine System (HMS) which consists only one way relationship between human and machine. As a part of mechatronics system, HAM has an ability to adapt with human skill improve the performance of machine. The example of application where HAM can be applied is driving a car. One of the important elements in HAM is the quantification of human skill. Thus, this project proposed a method to quantify the driving skill by using Artificial Neural Network (ANN) system. Feedforward neural network is used to create a multilayer neural network and five models of network were designed and tested using MATLAB Simulink software. Then, the best model from five models is chosen and compared with other method of quantification skill for verification. All simulation data are taken from M. Hafis Izran’s in his PhD thesis experiment. Based on results, the critical stage in designing the networks is to set the number of neurons in the hidden layer that affects an accuracy of the outputs. 2014-01 Thesis http://eprints.utm.my/id/eprint/43975/ http://eprints.utm.my/id/eprint/43975/1/MazleedaMazniMFKE2014.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 Mazni, Mazleenda Quantification of human driving skill for human adaptive mechatronics applications by using neural network system
description	Human Adaptive Mechatronic (HAM) is a two way relationship between human and machine. In HAM, not only human need to adapt the characteristics of machine but the machine also has to learn on human characteristic. HAM is an enhance system for Human Machine System (HMS) which consists only one way relationship between human and machine. As a part of mechatronics system, HAM has an ability to adapt with human skill improve the performance of machine. The example of application where HAM can be applied is driving a car. One of the important elements in HAM is the quantification of human skill. Thus, this project proposed a method to quantify the driving skill by using Artificial Neural Network (ANN) system. Feedforward neural network is used to create a multilayer neural network and five models of network were designed and tested using MATLAB Simulink software. Then, the best model from five models is chosen and compared with other method of quantification skill for verification. All simulation data are taken from M. Hafis Izran’s in his PhD thesis experiment. Based on results, the critical stage in designing the networks is to set the number of neurons in the hidden layer that affects an accuracy of the outputs.
format	Thesis
qualification_level	Master's degree
author	Mazni, Mazleenda
author_facet	Mazni, Mazleenda
author_sort	Mazni, Mazleenda
title	Quantification of human driving skill for human adaptive mechatronics applications by using neural network system
title_short	Quantification of human driving skill for human adaptive mechatronics applications by using neural network system
title_full	Quantification of human driving skill for human adaptive mechatronics applications by using neural network system
title_fullStr	Quantification of human driving skill for human adaptive mechatronics applications by using neural network system
title_full_unstemmed	Quantification of human driving skill for human adaptive mechatronics applications by using neural network system
title_sort	quantification of human driving skill for human adaptive mechatronics applications by using neural network system
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/43975/1/MazleedaMazniMFKE2014.pdf
_version_	1747817126856491008

Quantification of human driving skill for human adaptive mechatronics applications by using neural network system

Similar Items