By Topic

Soft computing techniques for intelligent classification system: a case study

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chakraborty, Basabi ; Dept. of Software & Inf. Sci., Iwate Prefectural Univ., Mura, Japan ; Chakraborty, G.

Soft computing techniques are becoming popular in designing real world industrial applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms etc., to develop hybrid intelligent autonomous systems that provide more flexibility by exploiting tolerance and uncertainty of real life situations. Intelligent classification systems are the most well known attempts. In this work a neuro fuzzy feature selector has been designed which is capable of extracting information in the form of fuzzy rules from numeric as well as non-numeric (linguistic) data. Conventional MLP and a variation of it have been used as the neural models and their performance has been compared by simulation with two different data sets. It is found that the proposed variation of the conventional MLP is better in respect to training time and classification accuracy

Published in:

Soft Computing Methods in Industrial Applications, 1999. SMCia/99. Proceedings of the 1999 IEEE Midnight-Sun Workshop on

Date of Conference: