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Case Study on Classification of Glass using Neural Network Tool in MATLAB

Published on March 2014 by Devika Chhachhiya, Amita Sharma, Manish Gupta
International Conference on Advances in Computer Engineering and Applications
Foundation of Computer Science USA
ICACEA - Number 3
March 2014
Authors: Devika Chhachhiya, Amita Sharma, Manish Gupta
0c069356-26a3-4454-974c-628888a25363

Devika Chhachhiya, Amita Sharma, Manish Gupta . Case Study on Classification of Glass using Neural Network Tool in MATLAB. International Conference on Advances in Computer Engineering and Applications. ICACEA, 3 (March 2014), 11-15.

@article{
author = { Devika Chhachhiya, Amita Sharma, Manish Gupta },
title = { Case Study on Classification of Glass using Neural Network Tool in MATLAB },
journal = { International Conference on Advances in Computer Engineering and Applications },
issue_date = { March 2014 },
volume = { ICACEA },
number = { 3 },
month = { March },
year = { 2014 },
issn = 0975-8887,
pages = { 11-15 },
numpages = 5,
url = { /proceedings/icacea/number3/15625-1419/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advances in Computer Engineering and Applications
%A Devika Chhachhiya
%A Amita Sharma
%A Manish Gupta
%T Case Study on Classification of Glass using Neural Network Tool in MATLAB
%J International Conference on Advances in Computer Engineering and Applications
%@ 0975-8887
%V ICACEA
%N 3
%P 11-15
%D 2014
%I International Journal of Computer Applications
Abstract

This paper encompasses application of the Neural Network Tool (NN Tool) in the glass classification problem and also discusses the correlation of the different activation functions with the Mean Square Error (MSE). This paper works on the glass data classification and finds the impact of different Activation functions on the error obtained while training and testing of the neural network model created by the NN Tool provided by the MATLAB Toolbox. Experiment was conducted on the MATLAB (NN tool) with glass data it has been observed that LOGSIG function gives the minimum MSE and gives more accurate results in comparison to the other activation functions provided by MATLAB (NN Tool). This paper highlights the relation of the nature of the dataset and the activation functions on the error obtained from the training of neural network model. In future by observing the limitations and effect of the different parameters such as number of hidden layers, activation functions, nature of the data, adjustments of weights, size of data and many more on the network modeling we will be able to understand and develop an improved algorithm and data mining tool for neural network classification technique with more accurate results.

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Index Terms

Computer Science
Information Sciences

Keywords

Neural Networks Data Mining Activation Function Matlab.