Sentence examples for solve uncertainty from inspiring English sources
"solve uncertainty" is a correct and usable phrase in written English
It is often used in the context of problem-solving and decision-making, especially when there are multiple factors or choices that must be considered. For example, "We must find a way to solve the uncertainty around the new regulations before the deadline."
It is one of the effective methods that are used to solve uncertainty problems under discrete data and partial information.
Application of the expert system shows that the proposed model can solve uncertainty reasoning problem, the reasoning results are more correct, and provide the theoretical support for expert system.
However, difficulty arises in constructing the BIN and quantifying the inference nodes to compute the inference through the nets and solve uncertainties.
The most common methods for solving uncertainty problems are to model the parameters as a random vector.
In this situation, new methods based on intelligent techniques such as fuzzy logic, neural networks, data aggregation, classification, clustering have been applied for solving uncertainty and imprecision related to sensory evaluation.
Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed framework for solving uncertainty quantification problems in flows through porous media using stationary and non-stationary permeability fields.
An alternative approach to large studies in solving uncertainty is to use CMA to monitor accumulation of treatment effects in clinical trials [ 38].
To solve this uncertainty, the current pixel value is compared to the last known background value at this pixel position.
A heuristic algorithm is developed to solve the uncertainty problem and generate a suitable decision for the provided facts.
To solve this uncertainty, we compare the current pixel value with the last known background value at this pixel position.
Huang [8] and Kim et al. [9] have proposed some works for applying fuzzy sets in clustering categorical data to solve the uncertainty issue.
