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...

Full description

Saved in:
Bibliographic Details
Main Author: Karman, Farida
Format: Thesis
Language:English
Published: 2008
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/43222/1/43222.PDF
Tags: Add Tag
No Tags, Be the first to tag this record!
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