AIMC Topic: Uncertainty

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Asymptotic Tracking Control for Uncertain MIMO Systems: A Biologically Inspired ESN Approach.

IEEE transactions on neural networks and learning systems
In this study, a biologically inspired echo state network (ESN)-based method is established for the asymptotic tracking control of a class of uncertain multi-input multi-output (MIMO) systems. By mimicking the characters of real biological systems, a...

Cloud-based neuro-fuzzy hydro-climatic model for water quality assessment under uncertainty and sensitivity.

Environmental science and pollution research international
River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When d...

A novel belief rule base expert system with interval-valued references.

Scientific reports
As an essential parameter in the belief rule base (BRB), referential values refer to evaluation criteria for describing attributes using quantitative data or linguistic terms, the rationality and preciseness of which are important to the modeling acc...

Optimistic reinforcement learning by forward Kullback-Leibler divergence optimization.

Neural networks : the official journal of the International Neural Network Society
This paper addresses a new interpretation of the traditional optimization method in reinforcement learning (RL) as optimization problems using reverse Kullback-Leibler (KL) divergence, and derives a new optimization method using forward KL divergence...

Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning.

Medical image analysis
Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is becoming an attractive solution in medical image segmentation. To make use of unlabeled data, current popular semi-supervised methods (e.g., temporal ensem...

Can uncertainty estimation predict segmentation performance in ultrasound bone imaging?

International journal of computer assisted radiology and surgery
PURPOSE: Segmenting bone surfaces in ultrasound (US) is a fundamental step in US-based computer-assisted orthopaedic surgeries. Neural network-based segmentation techniques are a natural choice for this, given promising results in related tasks. Howe...

Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images.

IEEE transactions on medical imaging
Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayes...

A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators.

Sensors (Basel, Switzerland)
Through this article, we present an advanced prescribed performance-tracking control system with finite-time convergence stability for uncertain robotic manipulators. It is therefore necessary to define a suitable performance function and error trans...

Bayesian deep learning-based H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout.

Magnetic resonance in medicine
PURPOSE: To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain.

Research and Forecast Analysis of Financial Stability for Policy Uncertainty.

Computational intelligence and neuroscience
The instability of financial market will have a great impact on money, bonds, and stocks and affect the economic development of society and people's lives. Therefore, it is very necessary for us to study and predict the financial stability. According...