Such sampling-based methods can work under mild system assumptions for both continuous-time and discrete-time systems, but they require the successor function to be evaluated once for each sample point. Sampling-based methods are, therefore, broadly applicable and also computationally expensive. The ...
Sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (i) definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (ii) generation of samples from uncertain analysis inputs, (iii) propagation of sampled ...
(2006). Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model. Human & Ecological Risk Assessment, (12), 1128-1152.Frey, H.C., and A. Moktari 2005. Evaluation of sampling-based methods for sensitivity analysis: case study...
In this work we present a system which can plan paths for industrial robots. We compare widely used sampling-based methods like PRM or RRT with Computational Intelligence (CI) based methods like genetic algorithms.Previous article in issue Next article in issue Keywords Path Planning Industrial ...
Monte Carlo and Quasi-Monte Carlo Methods 2010, Springer Proceedings in Mathematics & Statistics, vol. 23, pp. 557-572. Springer (2012)Maize, E., Sepikas, J., Spanier, J.: Accelerating the convergence of lattice methods by importance sampling-based transformations. In: Plaskota, L., ...
Mining labeled subgraph is a popular research task in data mining because of its potential application in many different scientific domains. All the existing methods for this task explicitly or implicitly solve the subgraph isomorphism task, which is computationally expensive, and thus they suffer from...
网络基于样本的策略 网络释义 1. 基于样本的策略 ...(Change-frequency-based)、基于样本的策略(Sampling-based)。 www.yscbook.com|基于 1 个网页 例句 释义: 全部,基于样本的策略 更多例句筛选
In this paper, we present a new sampling-based alpha matting approach for the accurate estimation of foreground and background layers of an image. Previous sampling-based methods typically rely on certain heuristics in collecting representative samples from known regions, and thus their performance det...
It uses Tensorflow (https://www.tensorflow.org/) as neural network framework and implements advanced sampling algorithms on top of it. It contains both a rapid prototyping platform for new sampling methods and also an analysis framework to understand the intricacies of the loss manifold in terms ...
Compared with other reliability methods, sampling-based methods have the benefits of avoiding errors from approximations of limit state surfaces, being insensitive to the complexity of limit-state functions, and being straightforward to implement. However, since many practical structural reliability problems...