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This article investigates the problem of robust exponential stability of fuzzy switched memristive inertial neural networks (FSMINNs) with time-varying delays on mode-dependent destabilizing impulsive control protocol. The memristive model presented here is treated as a switched system rather than employing the theory of differential inclusion and set-value map. To optimize the robust exponentially stable process and reduce the cost of time, hybrid mode-dependent destabilizing impulsive and adaptive feedback controllers are simultaneously applied to stabilize FSMINNs. In the new model, the multiple impulsive effects exist between two switched modes, and the multiple switched effects may also occur between two impulsive instants. Based on switched analysis techniques, the Takagi-Sugeno (T-S) fuzzy method, and the average dwell time, extended robust exponential stability conditions are derived. Finally, simulation is provided to illustrate the effectiveness of the results.
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http://dx.doi.org/10.1109/TNNLS.2020.2978542 | DOI Listing |
J Comput Graph Stat
March 2024
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint.
View Article and Find Full Text PDFACS Meas Sci Au
December 2024
Department of Bioengineering and Nano-Bioengineering, Research Center for Bio Materials and Process Development, Incheon National University, Incheon 22012, Republic of Korea.
Thermal cycling-based quantitative polymerase chain reaction (qPCR) represents the gold standard method for accurate and sensitive nucleic acid quantification in laboratory settings. However, its reliance on costly thermal cyclers limits the implementation of this technique for rapid point-of-care (POC) diagnostics. To address this, isothermal amplification techniques such as rolling circle amplification (RCA) have been developed, offering a simpler alternative that can operate without the need for sophisticated instrumentation.
View Article and Find Full Text PDFISA Trans
December 2024
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China. Electronic address:
The quadratic cost functions, exemplified by mean-square-error, often exhibit limited robustness and flexibility when confronted with impulsive noise contamination. In contrast, the generalized maximum correntropy (GMC) criterion, serving as a robust nonlinear similarity measure, offers superior performance in such scenarios. In this paper, we develop a recursive constrained adaptive filtering algorithm named recursive generalized maximum correntropy with a forgetting factor (FF-RCGMC).
View Article and Find Full Text PDFMed Phys
December 2024
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian, China.
Background: Due to the low signal-to-noise ratio (SNR) and the limited number of b-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between D and D easily leads to outliers and obvious graininess in estimated results.
Purpose: To propose a synthetic data driven supervised learning method (SDD-IVIM) for improving precision and noise robustness in IVIM parameter estimation without relying on real-world data for neural network training.
Methods: On account of the absence of standard IVIM parametric maps from real-world data, a novel model-based method for generating synthetic human brain IVIM data was introduced.
BMC Cancer
December 2024
Department of Epidemiology of School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Introduction: Cancer is a significant public health issue in Iran, and its incidence has been on the rise in recent years. The objective of this study is to predict the incidence of total cancer in Iran using a Bayesian age-period-cohort (APC) model.
Methods: Utilizing age-period-cohort modeling, this study assessed the multifaceted effects of age, period, and cohort on cancer incidence during the period spanning 2005 to 2017.
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