NDLI: Performance evaluation of fuzzy neural network with various aggregation operators

Content Provider	IEEE Xplore Digital Library
Author	Patil, P.M. Kulkarni, U.V. Sontakke, T.R.
Copyright Year	2002
Description	Author affiliation: Electron. & Comput. Sci. & Eng. Dept., SGGS Coll. of Eng. & Technol., Nanded, India (Patil, P.M.; Kulkarni, U.V.; Sontakke, T.R.)
Abstract	The modified fuzzy neural network (MFNN) proposed by Kulkarni and Sontakke is an extension of the fuzzy neural network (FNN) proposed by Kwan and Cai. Unlike FNN, the MFNN uses the Yager class of fuzzy union and intersection operators and works under a supervised environment. The paper describes MFNN with its learning algorithm. The MFNN is extended further and its performance is verified using various fuzzy aggregation operators. It is observed that Dubois and Prade operators give highest recognition rates, as compared to other operators for Fisher Iris data. Classification performance of the Hamacher operator is better with less number of neurons for Fisher Iris data, whereas the performance with the min-max operator for Wine data is better. Timing analysis is also performed and training time is found nearly equal for all the operators. The recall time per pattern is significantly less in the case of Schweizer and Sklar, and Hamacher operators. Thus, instead of using min-max or Yager class of operators one can tune the performance of the MFNN classifier to improve generalization performance by proper selection of aggregation operators.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	1744
Ending Page	1748
File Size	305013
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1198974
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Fuzzy neural networks Neural networks Fuzzy sets Neurons Unsupervised learning Clustering algorithms Topology Computer science Educational institutions Electronic mail
Content Type	Text
Resource Type	Article

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