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...

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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
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Summary: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 present a gradient-based learning method for Pi-Sigma network to train TSK fuzzy inference system. Moreover, some strong convergence results are established based on the weak convergence outcomes, which indicates that the sequence of weighted fuzzy parameters gets to a fixed point. Simulation results show the modified learning algorithm is effective to support the theoretical results.
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ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-015-9445-2