Multi-parameter online optimization algorithm of BP neural network algorithm in Internet of Things service

Wang, Pingquan; Liu, Xun; Zheng, Han

doi:10.1007/s00521-020-04913-8

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…It enables the company managers to take appropriate ex ante control and ex post control tools according to the actual situation. Ultimately, it can improve the risk response capability of Internet companies and enhance the social status of the company [4]. is paper mainly analyzes the theory related to financial risk prediction, selects suitable financial risk indicators, and then combines the principles of principal component analysis, particle swarm optimization algorithm, and artificial neural network to establish a particle swarm optimization BP neural network financial risk prediction model for listed companies based on the company's sample data and it uses different models to do comparative analysis on the prediction results of the company's financial risk, to provide some suggestions for the realistic enterprise [5].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Zhai¹

2021

Complexity

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This paper mainly analyzes the theories related to the financial risk of the company and combines the principles of principal component analysis, particle swarm optimization algorithm, and artificial neural network to derive the financial risk index system of the company. To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. The empirical results show that the model constructed by BP neural network not only has a high accuracy rate for static financial risk evaluation but also has a better prediction effect. After training and testing, the BP neural network-based enterprise financial risk evaluation model can accurately determine the existing financial situation of enterprise financial management and has a good prediction effect. Our research method is a fusion of the processing of the two methods, which belongs to the first integration of results.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Zhai¹

2021

Complexity

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“…is method is superior to other finger vein image matching methods and provides a significantly reduced template size (Xie and Kumar) [7]. Pradhan rough the algorithm simulation experiment, the absolute error between the predicted final moisture content and the measured moisture content is within 0.3, which speeds up the data collection time (Wang et al) [9]. Buckingham et al determine the potential of new data sources by analyzing the data, applications, and methods of gdelt to understand the changing events in the news media, help understand social changes, and detect large-scale environmental changes.…”

Section: Related Workmentioning

confidence: 99%

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Shen

2022

Mathematical Problems in Engineering

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With the rapid development of the information technology, mining news content efficiently is a difficult problem faced by the government and enterprises. The classification, clustering, and prediction of neural network are used in news mining. A keyword based on neural network and word vector are proposed as a text feature model, and the model is compared with other neural network models. This paper studies the vectorization of text for similarity recommendation and introduces two models: word vector model and text vector model doc2vec based on neural network. In the model with word vector as feature vector, the recommendation accuracy is about 75.35%, doc2vec model is about 44.5%, and the recommendation accuracy of the model with keyword as text is about 88.61%. The recommendation accuracy is about 91%, and the performance has greatly improved. The more the number of keywords, the better the recommendation effect of the model. When the number is about 20, the improvement of the recommendation effect tends to be gentle, and the continuous increase of the number of keywords will increase the operation time of the model. It is proposed that the keyword and word vector based on neural network together as the text vector model can more accurately mine news data, quickly obtain news information, and make prediction and early warning for many industries such as the real estate industry.

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“…At this time, the collection frequency can be reduced, and the collection and storage of low-value data can be reduced. (2) When the equipment changes, the monitoring value before and after the state is more significant than δ max , it means that the acquisition accuracy can no longer meet the user needs [5]. At this time, the acquisition frequency can be increased to improve the system's ability to capture changes in the device state.…”

Section: Collection Strategymentioning

confidence: 99%

“…(3) During the acquisition process, every step must be Judge whether Formula (1) is in [δ min , δ max ] , if it is, keep the original frequency and continue to collect; if it is out of tolerance, then use Formulas (2) and (3) to perform the out-of-tolerance judgment at the same time. (4) Use Formula (2), (3) When performing out-of-tolerance judgment, as long as one of the calculation results falls within [δ min , δ max ] , the system will keep the original frequency and continue to collect; if all are out of the allowable range, perform step (5). ( 5) If you use the formula, the calculation results of (1)-( 3) are all less than δ min , then reduce the acquisition frequency; if the three are more significant than δ max , then increase the acquisition frequency; otherwise, keep the original acquisition frequency.…”

Section: Algorithm Designmentioning

confidence: 99%

Computer wireless network integration technology in equipment state data collection

2021

J Wireless Com Network

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The paper introduces a handheld integrated power data acquisition and analysis equipment based on a computer wireless network mobile platform, an intelligent transportation inspection box. The intelligent transportation inspection box acts as a bridge between the transportation inspection work site side and the power grid company center to realize data and resource sharing between the site and center sides. The application of intelligent operation and inspection boxes based on computer wireless network integration technology is essential for improving the professional and intelligent level of on-site operation and maintenance, improving the efficiency of operation and inspection work and the ability to control equipment status, strengthening state maintenance and auxiliary decision-making, and promoting the construction of smart grids significance.

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Multi-parameter online optimization algorithm of BP neural network algorithm in Internet of Things service

Cited by 13 publications

References 12 publications

[Retracted] Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

[Retracted] Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Computer wireless network integration technology in equipment state data collection

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