Quantitative predictions of the physical state of the Earth's subsurface are routinely based on numerical solutions of complex coupled partial differential equations together with estimates of the uncertainties in the material parameters. The resulti...
Proceedings of the National Academy of Sciences of the United States of America
Oct 18, 2022
Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what data to consider next. For example, a data-driven approach for designi...
This article identifies a new upper bound norm for the intervalized interconnection matrices pertaining to delayed dynamical neural networks under the parameter uncertainties. By formulating the appropriate Lyapunov functional and slope-bounded activ...
Pseudo-inverse learners (PILs) are a kind of feedforward neural network trained with the pseudoinverse learning algorithm, which can be traced back to 1995 originally. PIL is an approach for nongradient descent learning, and its main advantage is the...
Artificial neural networks have demonstrated superiority over traditional computing architectures in tasks such as pattern classification and learning. However, they do not measure uncertainty in predictions, and hence they can make wrong predictions...
In this paper, the problem of an APPTMC for manipulators is investigated. During the robot's operation, the error states should be kept within an outlined range to ensure a steady-state and dynamic attitude. Firstly, we propose the modified PPFs. Aft...
Mathematical biosciences and engineering : MBE
Oct 10, 2022
The selection of an appropriate mining method is considered as an important tool in the mining design process. The adoption of a mining method can be regarded as a complex multi-attribute group decision-making (MAGDM) problem as it may contain uncert...
Neural networks : the official journal of the International Neural Network Society
Oct 10, 2022
This paper mainly attempts to discuss lag H synchronization in multiple state or derivative coupled reaction-diffusion neural networks without and with parameter uncertainties. Firstly, we respectively propose two types of reaction-diffusion neural n...
Computational intelligence and neuroscience
Oct 10, 2022
In the aggregation of uncertain information, it is very important to consider the interrelationship of the input information. Hamy mean (HM) is one of the fine tools to deal with such scenarios. This paper aims to extend the idea of the HM operator a...
Dual switching system is a special hybrid system that contains both deterministic and stochastic switching subsystems. Due to its complex switching mechanism, few studies have been conducted for dual switching systems, especially for systems with unc...
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