The Risk Early-Warning Model of Financial Operation in Family Farms Based on Back Propagation Neural Network Methods

Guan, Zhigui; Zhao, Yuanjun; Geng, Guojing

doi:10.1007/s10614-021-10134-5

Cited by 10 publications

(8 citation statements)

References 50 publications

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“…This problem can cause the weights of the layers to disappear during the training process and can no longer change. Only the data are weighted by the fixed weights causing interference to the training of the neural network [ 29 , 30 ]. Deep neural networks have similar timing with LSTM.…”

Section: Algorithm Designmentioning

confidence: 99%

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

2022

BioMed Research International

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The sudden arrival of the new crown epidemic has had a significant and long-lasting impact on the division’s economic environment as well as the production and operation activities of businesses. As far as the financial management is concerned, opportunities and difficulties are faced by enterprises of all types. With reference to the available research data, enterprises have an important contribution to GDP and jobs, but they still face a series of difficulties and challenges in their development in the context of the normalization of the epidemic. By analyzing the impact of the new crown pneumonia epidemic on the financial management work of enterprises, this paper proposes an artificial neural network-based enterprise financial forecasting and early warning method to provide an effective method for enterprise financial management. For the time-series characteristics of enterprise finances, a prediction model based on long- and short-term memory networks is developed which acknowledges the necessity of combining the temporal dimension with the spatial dimension for forecasting. This model incorporates time qualities into the data to the existing forecasting model. It also considers both working and nonworking day data and thoroughly considers the factors influencing corporate finance. Then, using BP neural network for financial risk prediction, nonfinancial index factors should be added to the financial early warning model thus eliminating the limitations of the financial early warning model. At the same time, the accuracy of the prediction can be improved which is more suitable for enterprises to apply in practice. The experimental results demonstrate that the financial prediction model built by multilayer feed forward neural networks and recurrent neural networks based on error back propagation training is inferior to the prediction model built by long- and short-term memory network. Regardless of the degree of fitting or prediction accuracy, the BP neural network model outperforms the conventional model for enterprise financial warning. Under the normalization of the pandemic, the combined use of both can offer an efficient technique for enterprise management.

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Section: Algorithm Designmentioning

confidence: 99%

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

2022

BioMed Research International

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“…Specifically, constructing a food security early warning platform can collect all kinds of data directly or indirectly related to food security and provide data sources for security status identification [ 52 , 53 ]. Through the data collection system, the platform can collect all kinds of data on food security.…”

Section: Methodsmentioning

confidence: 99%

Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy

Sun

Tang

Zhang

et al. 2022

IJERPH

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Since it affects a nation’s economy and people’s wellbeing, food security is a crucial national security requirement. In order to realize multi-angle grain data presentation and analysis and achieve the goal of deep mining, we propose a 3D dynamic visualization analysis method of multidimensional agricultural spatial–temporal data based on the self-organizing map. This method realizes the multi-angle display and analysis of grain data and achieves the purpose of deep mining. With the outbreak of COVID-19, the global food security situation is not optimistic, so it is necessary to use the food security early warning system to solve the food security issue. Machine learning has emerged widely in recent years and has been applied in various fields. Therefore, it is an excellent way to solve food security to apply the model in machine learning to construct a food security early warning system. Afterward, a food security early warning platform is developed with a support vector regression (SVR) model to ensure food security. Finally, we analyze China’s medium and long-term food security policy in line with modernization objectives. The experimental results show that the food security early warning platform based on the SVR model from 2007 to 2016 is effective compared with the actual situation every year. Through analyses, we should improve the stability, reliability, and sustainability of food supply, firmly hold the food security initiative, and construct a national food security guarantee system matching the goal of modernization.

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“…BP neural network is a kind of multilayer feed-forward network trained by error back propagation. Its basic idea is gradient descent method, and gradient search technology is used to minimize the error mean square deviation of the actual output value and the expected output value of the network [6].…”

Section: Bp Neural Network (Bpnn)mentioning

confidence: 99%

Prediction of CO and NOx Emissions from Automotive Engines Based on Machine Learning Algorithms

2021

AJEE

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In this paper, five models including BP neural network, Gaussian process regression, support vector machine regression, extreme learning machine and least squares support vector machine are used to model and predict the emissions of carbon monoxide and nitrogen oxides from automobile engines, and the performance of each model is compared. The input data set of this study includes nine parameters (ambient temperature, ambient pressure, ambient humidity, air filter pressure difference, gas turbine exhaust pressure, turbine inlet temperature, turbine outlet temperature, turbine energy yield, and compressor exhaust pressure), and the output data set includes two parameters (carbon monoxide and nitrogen oxides). The comparison of the research results shows that among the five machine learning algorithms, the Gaussian process regression model has the best fitting effect. The model has the highest prediction accuracy and the smallest error. Therefore, the Gaussian process regression model is used to model and predict the emissions of carbon monoxide and nitrogen oxides from automobile engines, and the optimal parameter values of the minimum emissions are found. Applying these automobile engine parameters to daily life is of great significance to alleviating air pollution.

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The Risk Early-Warning Model of Financial Operation in Family Farms Based on Back Propagation Neural Network Methods

Cited by 10 publications

References 50 publications

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

Study on the Practice of Enterprise Financial Management System under the Epidemic Norm Based on Artificial Neural Network

Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy

Prediction of CO and NOx Emissions from Automotive Engines Based on Machine Learning Algorithms

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