Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Adaptive Neural Fuzzy Inference System for Hydrogen Adsorption Prediction

Jufri Afiq Bin Jolan, Jufri Afiq (2009) Adaptive Neural Fuzzy Inference System for Hydrogen Adsorption Prediction. Universiti Teknologi Petronas, Sri Iskandar,Tronoh,Perak. (Unpublished)

[img]
Preview
PDF
Download (18Mb) | Preview

Abstract

This report is basically to discuss about the basic concept and implementation of Artificial Neural Fuzzy Inference System (ANFIS) in predicting the hydrogen adsorption isotherm. The objective of this project is to create an ANFIS that is able to predict the hydrogen adsorption isotherm. The challenge in this project is to develop the ANFIS that is able to predict the hydrogen adsorption isotherm at the highest accuracy. ANFIS is developed by using MatLab R2008a. This software which is a mathematicalp ower tool has the ability to developt he ANFIS. This is becauseth e software has the Fuzzy Logic Toolbox which is the basic requirement in building the system. The basic system is developed to receive two inputs which are temperature and pressure from users and gives one output which is the hydrogen adsorption value. Three membership functions are provided for each input which is then used in determining the output of the system. Multiple training data are given to the basic system in order to mature it. Upon completion, the system is then tested with test data and output from the system is analyzed.C alculationso f the output percentagee rror are carried out. From here,t he ANFIS membership functions for each input are fine tuned in order to reduce output percentagee rror in order to increaset he prediction accuracyo f the system. As a result, the ANFIS is able to give prediction data with an error less than 5% which desirable in this project

Item Type: Final Year Project
Subject: T Technology > TP Chemical technology
Divisions: Engineering > Chemical
Depositing User: Users 5 not found.
Date Deposited: 11 Jan 2012 12:24
Last Modified: 25 Jan 2017 09:44
URI: http://utpedia.utp.edu.my/id/eprint/542

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...