In this paper, we present a performance analysis of an evolutionary approach for fault identification in t-diagnosable systems, i.e. systems in which the set of t permanently faulty units could be unambiguously identi...
详细信息
In this paper, we present a performance analysis of an evolutionary approach for fault identification in t-diagnosable systems, i.e. systems in which the set of t permanently faulty units could be unambiguously identified. The proposed algorithm is based on evolutionary/genetic computing and is shown to correctly identify the set of faulty units. Simulation results are more than encouraging and should stimulate future research.
The increasing recognition of the association between adverse human health conditions and many environmental substances as well as processes has led to the need to monitor them. An important problem that arises in env...
详细信息
The increasing recognition of the association between adverse human health conditions and many environmental substances as well as processes has led to the need to monitor them. An important problem that arises in environmental statistics is the design of the locations of the monitoring stations for those environmental processes of interest. One particular design criterion for monitoring networks that tries to reduce the uncertainty about predictions of unseen processes is called the maximum-entropy design. However, this design criterion involves a hard optimization problem that is computationally intractable for large data sets. Previous work of Wang et al. (2017) examined a probabilistic model that can be implemented efficiently to approximate the underlying optimization problem. In this paper, we attempt to establish statistically sound tools for assessing the quality of the approximations. (C) 2020 Elsevier B.V. All rights reserved.
暂无评论