Sentence examples for describing uncertain from inspiring English sources

This poses a dilemma of excessive precision in describing uncertain phenomenon.

We explore the relation between the development of a non-negligible probability of negative states and the instability of numerical integration of the intrusive Galerkin ordinary differential equation system describing uncertain chemical ignition.

The proposed framework is based on an interval analysis approach, which along with max-plus algebra, allows describing uncertain discrete event system such as the production one being considered in this paper.

In order to describe uncertain variables, Liu [21] introduced a new concept: uncertainty distribution.

This paper presents a fuzzy chance-constrained scheduling model which utilizes fuzzy variables to describe uncertain features of distributed generators (DGs).

The crucial technical challenge in model validation is limited data, thus the non-probabilistic interval model is adopted to describe uncertain parameters.

They consider continuous distribution functions (normal and gamma distribution) to describe uncertain parameters and apply Monte-Carlo sampling-based techniques, sample average approximation (SAA), to approximate them by discrete set of scenarios.

Firstly, by virtue of the theory of non-probabilistic interval process, an interval process model of fatigue crack propagation is investigated, in which we describe uncertain crack length a(N) at any load cycle N as interval variable and define the corresponding auto-covariance function and the correlation coefficient function to further characterize the correlation of a(N) at different cycles.

The paper proposes a framework, which involves using an interval model for describing the uncertain or variable dynamics of the process.

The basic idea is to achieve a dimensionality reduction of the input ANN training space by using as input vector the random phase angles of the spectral representation method instead of the random variables describing the uncertain input parameters.

Karer and Skrjanc [23] proposed a robust optimization framework for PID controllers by describing the uncertain dynamics of the process as an interval model, which was firstly transformed into a deterministic model and then solved by a particle swarm optimization algorithm.