Adaptive Interval Type-2 Fuzzy Neural Network Sliding Mode Control of Nonlinear Systems Using Improved Extended State Observer

Liu, Lunhaojie; Fei, Juntao; Yang, Xianghua

doi:10.3390/math11030605

Cited by 6 publications

(1 citation statement)

References 43 publications

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“…Thus, these type-1 FNNs struggle to tackle the uncertainty of rules. To tackle uncertainties (such as noise measurements, semantics variations, and so on), the type-2 FNNs are presented [28] and have excellent performance [29][30][31]. Unfortunately, due to type-reduction procedure (such as Karnik-Mendel iteration [32]) from type-2 to type-1, significant additional computational costs will incur for type-2 FNNs.…”

Section: Introductionmentioning

confidence: 99%

First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

Zhang,

Wang,

Gong

et al. 2023

Mathematics

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Nonstationary fuzzy inference systems (NFIS) are able to tackle uncertainties and avoid the difficulty of type-reduction operation. Combining NFIS and neural network, a first-order sparse TSK nonstationary fuzzy neural network (SNFNN-1) is proposed in this paper to improve the interpretability/translatability of neural networks and the self-learning ability of fuzzy rules/sets. The whole architecture of SNFNN-1 can be considered as an integrated model of multiple sub-networks with a variation in center, variation in width or variation in noise. Thus, it is able to model both “intraexpert” and “interexpert” variability. There are two techniques adopted in this network: the Mean Shift-based fuzzy partition and the Group Lasso-based rule selection, which can adaptively generate a suitable number of clusters and select important fuzzy rules, respectively. Quantitative experiments on six UCI datasets demonstrate the effectiveness and robustness of the proposed model.

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