AIMC Topic: Time Factors

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Finite-time bipartite synchronization of switched competitive neural networks with time delay via quantized control.

ISA transactions
This article tackles the finite-time bipartite synchronization (FTBS) of coupled competitive neural networks (CNNs) with switching parameters and time delay. Quantized control is utilized to achieve the FTBS at a small control cost and with limited c...

Exponential passivity of discrete-time switched neural networks with transmission delays via an event-triggered sliding mode control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the exponential passivity of discrete-time switched neural networks (DSNNs) with transmission delays via an event-triggered sliding mode control (SMC). Firstly, a novel discrete-time switched SMC scheme is constructed on the b...

Periodicity and multi-periodicity generated by impulses control in delayed Cohen-Grossberg-type neural networks with discontinuous activations.

Neural networks : the official journal of the International Neural Network Society
This paper discusses the periodicity and multi-periodicity in delayed Cohen-Grossberg-type neural networks (CGNNs) possessing impulsive effects, whose activation functions possess discontinuities and are allowed to be unbounded or nonmonotonic. Based...

Synchronization of recurrent neural networks with unbounded delays and time-varying coefficients via generalized differential inequalities.

Neural networks : the official journal of the International Neural Network Society
In this paper, we revisit the drive-response synchronization of a class of recurrent neural networks with unbounded delays and time-varying coefficients, contrary to usual in the literature about time-varying neural networks, the signs of self-feedba...

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

Nature communications
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...

Aging-related markers in rat urine revealed by dynamic metabolic profiling using machine learning.

Aging
The process of aging and metabolism is intimately intertwined; thus, developing biomarkers related to metabolism is critical for delaying aging. However, few studies have identified reliable markers that reflect aging trajectories based on machine le...

Learning on knowledge graph dynamics provides an early warning of impactful research.

Nature biotechnology
The scientific ecosystem relies on citation-based metrics that provide only imperfect, inconsistent and easily manipulated measures of research quality. Here we describe DELPHI (Dynamic Early-warning by Learning to Predict High Impact), a framework t...

H synchronization of delayed neural networks via event-triggered dynamic output control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates H exponential synchronization (ES) of neural networks (NNs) with delay by designing an event-triggered dynamic output feedback controller (ETDOFC). The ETDOFC is flexible in practice since it is applicable to both full order a...