Public export credit insurance in the Netherlands: An input–output approach

Berg, Marcel van den; Beveren, Ilke Van; Lemmers, Oscar; Span, Tommy; Walker, Adam N.

doi:10.1111/twec.12824

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the restricted Boltzmann machine is proposed to solve the problem of divergence. It greatly improves the learning performance only by connecting the adjacent two layers [30]. In the 21-st century, some scholars propose a hierarchical RBM deep belief network algorithm based on GL (greedy learning) and study the learning ability of neural networks with multiple hidden layers.…”

Section: Deep Belief Networkmentioning

confidence: 99%

Application of Deep Belief Network for Sustainable Development via Deep Learning to Export Credit Risk Assessment Under the Belt and Road Strategy

Li,

Pu,

Song

et al. 2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

The purpose of the study is to reduce the export credit risk of enterprises, and the export credit risk assessment of enterprises under the belt and road strategy based on deep learning is discussed. First, the research background and deep belief networks are introduced. Second, the contrast divergence algorithm based on the deep belief model is improved on the restricted Boltzmann machine. Finally, the deep belief network of classification and partition is constructed and simulated. The results show that the test accuracy of the classification and partition of the restricted Boltzmann machine (CPRBM) constructed is higher than that of the restricted Boltzmann machine (RBM). When the accuracy of the algorithm is verified under the condition of unbalanced two classification samples in a relatively small amount of datasets, the accuracy of the CPRBM algorithm is 93.71%, and the accuracy of the RBM algorithm is 89.86%. In the optimization stage of the deep belief networks, the convergence rate of the CPRBM is slower than that of the RBM. Since the optimized system increases the penalty term in the first training stage of the deep belief networks, the penalty term is canceled in the second stage of optimization. At three time points, the algorithm accuracy of the CPRBM is higher than that of the RBM. The simulation results are consistent with the previous experimental results. Although the accuracy is not high at the third time point, the CPRBM algorithm still has some advantages. Compared with the accuracy of the support vector machine (SVM) and the deep extreme learning machine (DELM), the CPRBM algorithm based on deep belief networks has the highest accuracy at any time point. The CPRBM algorithm constructed has obvious advantages compared with common models, and the overall performance of the algorithm is better. The conclusions provide the support for the sustainable development of the economy under the Belt and Road strategy.

show abstract

Section: Deep Belief Networkmentioning

confidence: 99%