Adaptive Neural Fuzzy Inference System for Hydrogen Adsorption Prediction

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