Convergence analysis of the batch gradient-based neuro-fuzzy learning algorithm with smoothing L1/2 regularization for the first-order Takagi–Sugeno system

It has been proven that Takagi–Sugeno systems are universal approximators, and they are applied widely to classification and regression problems. The main challenges of these models are convergence analysis and their computational complexity due to the large number of connections and the pruning of...

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Bibliographic Details

Published in:	Fuzzy sets and systems Vol. 319; pp. 28 - 49
Main Authors:	Liu, Yan, Yang, Dakun
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 15.07.2017
Subjects:	Convergence First-order Takagi–Sugeno inference system Pi–Sigma network Smoothing [formula omitted] regularization First-order Takagi–Sugeno inference system Pi–Sigma network Convergence Smoothing L1/2 regularization
ISSN:	0165-0114, 1872-6801
Online Access:	Get full text
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