AIMC Topic: Uncertainty

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Command-Filtered Robust Adaptive NN Control With the Prescribed Performance for the 3-D Trajectory Tracking of Underactuated AUVs.

IEEE transactions on neural networks and learning systems Oct 27, 2022
A novel robust adaptive neural network (NN) control scheme with prescribed performance is developed for the 3-D trajectory tracking of underactuated autonomous underwater vehicles (AUVs) with uncertain dynamics and unknown disturbances using new pres...
Neural Networks, Computer Computer Simulation Nonlinear Dynamics Uncertainty
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Neural Adaptive Self-Triggered Control for Uncertain Nonlinear Systems With Input Hysteresis.

IEEE transactions on neural networks and learning systems Oct 27, 2022
The issue of neural adaptive self-triggered tracking control for uncertain nonlinear systems with input hysteresis is considered. Combining radial basis function neural networks (RBFNNs) and adaptive backstepping technique, an adaptive self-triggered...
Neural Networks, Computer Computer Simulation Nonlinear Dynamics Uncertainty
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Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space.

International journal of molecular sciences Oct 26, 2022
Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equati...
Artificial Intelligence Phenotype Drug Discovery Drug Repositioning Uncertainty
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Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models.

Environmental science and pollution research international Oct 20, 2022
Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex an...
Neural Networks, Computer Algorithms Triticum Spatial Analysis Uncertainty
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Stability analysis with general fuzzy measure: An application to social security organizations.

PloS one Oct 20, 2022
An effective method for evaluating the efficiency of peer decision-making units (DMUs) is data envelope analysis (DEA). In engineering sciences and real-world management problems, uncertainty in input and output data always exists. To achieve reliabl...
Organizations Fuzzy Logic Probability Uncertainty Social Security
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3D multi-physics uncertainty quantification using physics-based machine learning.

Scientific reports Oct 19, 2022
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...
Machine Learning Uncertainty Physics
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Conformal prediction under feedback covariate shift for biomolecular design.

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...
Machine Learning Algorithms Feedback Molecular Conformation Uncertainty
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New Global Asymptotic Robust Stability of Dynamical Delayed Neural Networks via Intervalized Interconnection Matrices.

IEEE transactions on cybernetics Oct 17, 2022
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...
Neural Networks, Computer Algorithms Time Factors Uncertainty
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Bayesian Pseudoinverse Learners: From Uncertainty to Deterministic Learning.

IEEE transactions on cybernetics Oct 17, 2022
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...
Neural Networks, Computer Algorithms Bayes Theorem Uncertainty
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Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks.

Nature communications Oct 17, 2022
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...
Neural Networks, Computer Bayes Theorem Neurons Synapses Uncertainty
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