A deep learned type-2 fuzzy neural network: Singular value decomposition approach

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.