•  
  •  
 

International Journal of Computer and Communication Technology

Abstract

Classification is one of the most active research and application areas of artificial neural networks (ANN). One of the difficulties in using ANN is to find the most suitable combination of training, learning and transfer function for classification of data sets with increasing number of features and classified sets. In this paper we have studied the effect of different combinations of functions while using artificial neural network as a classifier and analyzed the suitability of these functions for different kinds of datasets. The appropriateness of the proposed work has been determined on the basis of mean square error, rate of convergence, and accuracy of the classified dataset. Our inferences are based on the simulation results over the datasets used. .

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.