Sentence examples for modelling methods such from inspiring English sources

Novel spatial modelling methods such as maximum entropy (MAXENT) and the genetic algorithm for rule set production (GARP) require only disease presence data and have been used extensively in the fields of ecology and conservation, to model species distribution and habitat suitability [ 19].

In this study, an integrated framework as well as the design steps are proposed for the design of all kinds of formulated products including liquids, solids, emulsions, etc. Strategies for the selection of different modelling methods such as rule-based methods, model-based methods, experiments and databases are also presented.

The artificial neural network outperforms other well-known modelling methods, such as the radial basis function neural network and polynomial regression, for the global mechanical strength modelling, and the probability distribution obtained from the proposed uncertainty evaluation procedure is shown to be accurate.

Structures' response of the DS is deterministic in nature and analysed using deterministic modelling methods such as finite element (FE) method.

Modelling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modelling examples.

However current numerical modelling methods, such as Computational Fluid Dynamics (CFD), cannot balance modelling cost with result accuracy to satisfy the requirements of exploratory urban design at the neighborhood scale.

The proposed method is compared with traditional reduced-order modeling methods such as component mode synthesis, and its advantages are discussed.

Typical speech modeling methods such as STP and LTP are good candidates for the pre-processing module.

Fully automated topic modeling methods such as Latent Dirichlet Allocation [123] might be used to create a sustainability vocabulary database.

Conventional speaker modeling methods such as Gaussian mixture models (GMMs) [1] achieve very high performance for speaker identification and verification tasks on high-quality data when training and testing conditions are well controlled.

Compared with other ab initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of I-TASSER is either much better or is similar within a lower computational time.