Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman

In this project, Hybrid Multilayer Perceptron (HMLP) neural network is used for system modeling to improve the speed and the convergence in training process. This project requires collecting of a raw material data from controlling devices Proportional Integral and Derivative (PID) control system fro...

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Main Author:	Karman, Farida
Format:	Thesis
Language:	English
Published:	2008
Subjects:	Instruments and machines Computer software Neural networks (Computer science) Malaysia
Online Access:	https://ir.uitm.edu.my/id/eprint/43222/1/43222.PDF
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id	my-uitm-ir.43222
record_format	uketd_dc
spelling	my-uitm-ir.432222021-03-11T03:00:57Z Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman 2008-04 Karman, Farida Instruments and machines Electronic Computers. Computer Science Computer software Capability maturity model (Computer software). Software engineering Neural networks (Computer science) Malaysia In this project, Hybrid Multilayer Perceptron (HMLP) neural network is used for system modeling to improve the speed and the convergence in training process. This project requires collecting of a raw material data from controlling devices Proportional Integral and Derivative (PID) control system from Modular Servo control system (MS150).The HMLP neural network program is designed using MATLAB. The HMLP network is trained using a Modified Recursive Prediction Error (MRPE) algorithm to obtain the appropriate parameter for the network. Based on the analysis of performance, the developed system is able to achieve high accuracy and minimum error. The accuracy is at the rate of 99.58%, while the error and Mean Square Error (MSE) are at the rate of 0.42% and 4.9840e-8. The analysis of the performance of the HMLP network has proven that it is suitable to be used in system modeling. The network strategy employed will result in fast speed of convergence rate if compared to the Multilayer Perceptron (MLP) network, but low speed in program running time because of the HMLP structure is more complex than MLP network. The HMLP network also indicates that the network models adequately represents the systems dynamic. 2008-04 Thesis https://ir.uitm.edu.my/id/eprint/43222/ https://ir.uitm.edu.my/id/eprint/43222/1/43222.PDF text en public degree Universiti Teknologi MARA Faculty of Electrical Engineering Yahaya, Saiful Zaimy
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Yahaya, Saiful Zaimy
topic	Instruments and machines Instruments and machines Computer software Instruments and machines Neural networks (Computer science) Malaysia
spellingShingle	Instruments and machines Instruments and machines Computer software Instruments and machines Neural networks (Computer science) Malaysia Karman, Farida Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman
description	In this project, Hybrid Multilayer Perceptron (HMLP) neural network is used for system modeling to improve the speed and the convergence in training process. This project requires collecting of a raw material data from controlling devices Proportional Integral and Derivative (PID) control system from Modular Servo control system (MS150).The HMLP neural network program is designed using MATLAB. The HMLP network is trained using a Modified Recursive Prediction Error (MRPE) algorithm to obtain the appropriate parameter for the network. Based on the analysis of performance, the developed system is able to achieve high accuracy and minimum error. The accuracy is at the rate of 99.58%, while the error and Mean Square Error (MSE) are at the rate of 0.42% and 4.9840e-8. The analysis of the performance of the HMLP network has proven that it is suitable to be used in system modeling. The network strategy employed will result in fast speed of convergence rate if compared to the Multilayer Perceptron (MLP) network, but low speed in program running time because of the HMLP structure is more complex than MLP network. The HMLP network also indicates that the network models adequately represents the systems dynamic.
format	Thesis
qualification_level	Bachelor degree
author	Karman, Farida
author_facet	Karman, Farida
author_sort	Karman, Farida
title	Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman
title_short	Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman
title_full	Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman
title_fullStr	Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman
title_full_unstemmed	Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman
title_sort	improve in speed and the convergence in training process for the purpose of system modeling using hybrid multilayer perceptron (hmlp) neural network / farida karman
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2008
url	https://ir.uitm.edu.my/id/eprint/43222/1/43222.PDF
_version_	1783734683751677952

Improve in speed and the convergence in training process for the purpose of system modeling using Hybrid Multilayer Perceptron (HMLP) neural network / Farida Karman

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