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Volume: 3 Issue: 3

Classification of Fuzzy-Based Information using Improved Backpropagation Algorithm of Artificial Neural Networks

Mukul Jain, P.K. Butey, Manu Pratap Singh

Abstract:
Artificial neural networks show inadequacy while classifying fuzzy based information. In this paper, a methodology has presented for adequate classification of fuzzy information. In this system, the fuzzy rules are used as inputoutput stimuli’s. The system consists of layered architecture of fuzzy-neural network. First hidden layer generates the degree of membership for all the input-output patterns pairs. This vector of degree of membership is now being trained into the neural network for generating the final classification of rules using the Backpropagation algorithm of neural network. Thus, neural network as a whole performs two tasks. First it generates the degree of membership and later it classifies the rules in different classes on the basis of membership function. A simulation program in C has been deliberated and developed for analyzing the consequences. The overall process has been illustrated by applying to two real-world classification problems i.e. IRIS and Post-operative Patient Data. Results show the adequacy of the classification and improvement in the epochs for learning the fuzzy rules.

Keywords:
Pattern Classification, Fuzzy System, Artificial Neural Networks, Fuzzy Logic.

doi:10.5019/j.ijcir.2004.108

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