Paper Info
Title
A Deep Learned Type-2 Fuzzy Neural Network: Singular Value Decomposition Approach
Abstract
The main objective of this study is to present a novel dynamic fractional-order deep learned type 2 fuzzy logic system (FDT2-FLS) with improved estimation capability. The proposed FDT2-FLS is constructed based on the criteria of singular value decomposition and uncertainty bounds type reduction. The upper and the lower singular values of the set of inputs are estimated by a simple filter and the output is obtained by fractional-order integral of the uncertainty bounds type-reduction. Using stability criteria of fractional-order systems, the adaptation rules of the consequent parameters are extracted such that the globally Mittag-Leffler stability is achieved. The proposed FDT2-FLS is employed for online dynamic identification of a hyperchaotic system, online prediction of chaotic time series and online prediction of glucose level in type-1 diabetes patients and its performance is compared with other well-known methods. It is shown that the proposed mechanism results in significantly better prediction and estimation performance with less tunable parameters in just one learning epoch. (C) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.asoc.2021.107244
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Type-2 fuzzy neural network, Deep learned, Singular value decomposition, Mittag-Leffler stability and uncertainty, bounds type-reduction
Journal
105
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Sultan Noman Qasem100.34
Ardashir Mohammadzadeh223.42