2023
DOI: 10.3389/fenrg.2022.1087526
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Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

Abstract: The concept of low-carbon economic development has led to changes in the business environment and financial environment of enterprises, leading to increased financial risks faced by enterprises. How to help enterprises better warn, prevent and control financial risks from the perspective of low-carbon economy has become a hot issue worth studying. Based on this, this paper is based on the perspective of low carbon economy, on the basis of analyzing the financing risk, investment risk, capital operation risk an… Show more

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Cited by 6 publications
(3 citation statements)
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“…Neural network models are widely used in various fields such as prediction, pattern recognition, and risk assessment [16][17][18]. The BP neural network model is a multi-layer feedforward network trained according to error backpropagation [19].…”
Section: Construction Of a Carbon Dioxide Prediction Modelmentioning
confidence: 99%
“…Neural network models are widely used in various fields such as prediction, pattern recognition, and risk assessment [16][17][18]. The BP neural network model is a multi-layer feedforward network trained according to error backpropagation [19].…”
Section: Construction Of a Carbon Dioxide Prediction Modelmentioning
confidence: 99%
“…In the process of learning, information can be transmitted in the forward direction while error can be transmitted in the reverse direction, and each layer can also be interconnected with each other. The acceptance of information can be determined by the connection weight of the network (Wan and Yu, 2023). The specific workflow is: the neurons in the input layer acquire the signal from the outside and receive it, and then transmit it to the hidden layer.…”
Section: Bp Neural Network Modelmentioning
confidence: 99%
“…This paradigm shift finds validation across multiple research studies. For instance, studies have showcased the effectiveness of deep learning models such as BP neural networks (Cai et al, 2020;Wu et al, 2021;Liu, 2022;Wan and Yu, 2023) and Multi layer perception (MLP) (Lian et al, 2021;Li et al, 2022;Wang and Zhang, 2023). The appeal of these deep learning models lies in their exceptional performance and versatility.…”
Section: Introductionmentioning
confidence: 99%