Strong Convergence Analysis of Batch Gradient-Based Learning Algorithm for Training Pi-Sigma Network Based on TSK Fuzzy Models

By combining of the benefits of high-order network and TSK (Tagaki-Sugeno-Kang) inference system, Pi-Sigma network is capable to dispose with the nonlinear problems much more effectively, which means it has a compacter construction, and quicker computational speed. The aim of this paper is to presen...

Full description

Bibliographic Details

Published in:	Neural processing letters Vol. 43; no. 3; pp. 745 - 758
Main Authors:	Liu, Yan, Yang, Dakun, Nan, Nan, Guo, Li, Zhang, Jianjun
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.06.2016 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Complex Systems Computational Intelligence Computer Science Convergence Inference Machine learning Neural networks Parameter modification Teaching methods Pi-sigma network TSK fuzzy models Strong convergence Gradient-based learning algorithm
ISSN:	1370-4621, 1573-773X
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!