Global exponential synchronization of interval neural networks with mixed delays via delayed impulsive control
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.09.010
Abstract: Abstract This paper investigates the synchronization problem of interval neural networks with both time-varying and unbounded distributed delays under delayed impulsive control. A new impulsive differential inequality which considers the effect of time delays in… read more here.
Keywords: impulsive control; delayed impulsive; interval neural; neural networks ... See more keywords
Instability in deep learning – when algorithms cannot compute uncertainty quantifications for neural networks
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DOI: 10.1017/s095679252510017x
Abstract: Abstract In deep learning, interval neural networks are used to quantify the uncertainty of a pre-trained neural network. Suppose we are given a computational problem $P$ and a pre-trained neural network $\Phi _P$ that aims… read more here.
Keywords: deep learning; phi; uncertainty; interval neural ... See more keywords
Modeling Uncertain Dynamic Plants With Interval Neural Networks by Bounded-Error Data
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DOI: 10.1109/access.2020.2964835
Abstract: This paper presents a novel approach to building an interval dynamic model for an industrial plant with uncertainty by an interval neural network (INN). A new type of randomized learner model, named interval random vector… read more here.
Keywords: error data; uncertain dynamic; modeling uncertain; interval neural ... See more keywords
Sample-Based Continuous Approximate Method for Constructing Interval Neural Network
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2024.3409379
Abstract: In safety-critical engineering applications, such as robust prediction against adversarial noise, it is necessary to quantify neural networks’ uncertainty. Interval neural networks (INNs) are effective models for uncertainty quantification, giving an interval of predictions instead… read more here.
Keywords: sample based; constrained optimization; chance constrained; interval neural ... See more keywords