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APPLICATION OF bp neural network model IN SUPPLY CHAIN FINANCIAL RISK CONTROL

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2024年第5期20卷 1509-1521页

作者： Fu, Haoran Li, Huahui Anyang Normal Univ Sch Econ 436 Xiange Ave Anyang 455000 Peoples R China Univ Sains Malaysia Sch Management Minden 11800 Penang Malaysia

. The diversification of the market development model has rapidly promoted financial innovation, and the improvement of the supply chain has also given rise to the supply chain financial form. However, the risk problem also arises. In order to effectively control supply chain financial risk and improve the ability of supply chain enterprises to resist financial crises, the application of the bp neural network model in supply chain financial risk control is studied. The bp neural network model is designed to categorize the dynamic risk types of supply chain finance according to the dynamic development characteristics of supply chain finance in the past, with the corresponding weight distribution allocated to each category. The bp neural network model is employed to extract the financial risk characteristics of the supply chain, and a dynamic measurement of supply chain finance is conducted based on the risk characteristics. This paper constructs a supply chain financial risk management model based on the bp neural network model and controls the supply chain financial risk. The experimental results demonstrate that after employing various control methods, the risk exhibits a declining trend, effectively mitigating the financial risk of the supply chain with high efficacy.

关键词： bp neural network model Supply chain Financial risk Risk control

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Predicting the sound speed of seafloor sediments in the Middle area of the Southern Yellow Sea based on a bp neural network model

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MARINE GEORESOURCES & GEOTECHNOLOGY 2023年第6期41卷 662-670页

作者： Chen, Mujun Meng, Xiangmei Kan, Guangming Wang, Jingqiang Li, Guanbao Liu, Baohua Liu, Chenguang Liu, Yanguang Meng, Wenjing Minist Nat Resources Key Lab Marine Geol & Metallogeny Inst Oceanog 1 Qingdao Peoples R China Pilot Natl Lab Marine Sci & Technol Qingdao Lab Marine Geol Qingdao Peoples R China Shandong Univ Sci & Technol Coll Earth Sci & Engn Qingdao Peoples R China

Based on the data of the sound speed and the physical-mechanical properties of 300 seafloor sediment samples collected in the middle area of the Southern Yellow Sea, a backpropagation (bp) neural network algorithm is used herein to study the neural network topology and the corresponding neuron characteristic factors. A neural network model for predicting the sound speed of the seafloor sediment based on density, water content, porosity, void ratio, liquid limit, plastic limit, and clay content in the middle area of the Southern Yellow Sea is established. The results show that the sound speed prediction accuracy is the highest when the network model contains seven input layer neurons, ten hidden layer neurons, and one output node. According to the prediction results, the MAE(Mean Absolute Error) and the MAPE(Mean Absolute Percentage Error) are 13.19 m s(-1) and 0.85%, respectively. The prediction results illustrate that the established bp neural network prediction method for the seafloor sediment sound speed is better than the traditional single, two and multi-parameter prediction equations.

关键词： sound speed bp neural network model seafloor sediment single-parameter prediction equation middle area of the Southern Yellow Sea

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Shallow Water Bathymetry Mapping from ICESat-2 and Sentinel-2 Based on bp neural network model

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WATER 2022年第23期14卷 3862-3862页

作者： Guo, Xiaozu Jin, Xiaoyi Jin, Shuanggen Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomatics Engn Nanjing 210044 Peoples R China Chinese Acad Sci Shanghai Astron Observ Shanghai 200030 Peoples R China Henan Polytech Univ Sch Surveying & Land Informat Engn Jiaozuo 454000 Peoples R China

Accurate shallow water bathymetry data are essential for coastal construction and management, marine traffic, and shipping. With the development of remote sensing satellites and sensors, the satellite-derived bathymetry (SDB) method has been widely used for bathymetry in shallow water areas. However, traditional satellite bathymetry requires in-situ bathymetric data. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with the advanced high-resolution topographic laser altimeter system (ATLAS) provides a new technical tool and makes up for the shortcomings of traditional bathymetric methods in shallow waters. In this study, a new method is proposed to automatically detect photons reflected from the shallow seafloor with ICESat-2 altimetry data. Two satellite bathymetry models were trained, to obtain shallow water depth from Sentinel-2 satellite images. First, sea surface and seafloor signal photons from ICESat-2 were detected in the Oahu (in the U.S. Hawaiian Islands) and St. Thomas (in the U.S. Virgin Islands) sampling areas, to obtain water depths along the surface track. The results show that the RMSE is between 0.35 and 0.71 m and the R-2 is greater than 0.92, when compared to the airborne LiDAR bathymetry (ALB) data in the field. Second, the ICESat-2 bathymetric points from Oahu Island are used to train the Back Propagation (bp) neural network model and obtain the SDB. The RMSE is between 0.97 and 1.43 m and the R-2 is between 0.90 and 0.96, which are better than the multi-band ratio model with RMSE of 1.03-1.57 m and R-2 of 0.89-0.95. The results show that the bp neural network model can effectively improve bathymetric accuracy, when compared to the traditional multi-band ratio model. This approach can obtain shallow water bathymetry more easily, without the in-situ bathymetric data. Therefore, it extends to a greater extent with the free ICESat-2 and Sentinel-2 satellite data for bathymetry in shallow water areas, such as coastal, island and inland water bo

关键词： ICESat-2 Sentinel-2 shallow water bathymetry bp neural network model multi-band ratio model

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Research on the initial scale of The Belt and Road International Earthquake Cooperation Fund Based on VaR method of bp neural network model of big data 3

Research on the initial scale of The Belt and Road Internati...

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3rd International Conference on Electronics and Communication;network and Computer Technology (ECNCT)

作者： Deng, Guo-qu Han, Qian Li, Can Chen, Hu Henan Univ Sci & Technol Sch Management Luoyang 471023 Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

The area along The Belt and Road is located in the Mediterranean Sea-the Himalaya Seismic Belt and the Pacific Rim Seismic Belt, which is one of the regions with the most frequent earthquake disasters and the most severe losses in the world. The establishment of The Belt and Road earthquake international cooperation fund can help maintain regional society peace, stability and prosperity. Based on The Belt and Road earthquake data of EM-DAT,this paper uses the neural network model VaR method to calculate the fund size of The Belt and Road earthquake international cooperation fund under different risk tolerances. According to the research results, when the risk tolerance is 90%, 95%, 99%, and 99.5%, the initial fund sizes are predicted to be 250.8 million U.S. dollars, 444.8 million U.S. dollars, 633.2 million U.S. dollars, and 910.8 million U.S. dollars, respectively.

关键词： The Belt and Road International Earthquake Cooperation Fund bp neural network model

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Construction and verification of retinal vessel segmentation algorithm for color fundus image under bp neural network model

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JOURNAL OF SUPERCOMPUTING 2021年第4期77卷 3870-3884页

作者： Tang, Siyuan Yu, Feifei Inner Mongolia Univ Sci & Technol Sch Baotou Med Coll Baotou 014060 Peoples R China Shaoxing Univ Dept Ophthalmol Zhuji Affiliated Hosp Zhuji Peoples R China

To improve the accuracy of retinal vessel segmentation, a retinal vessel segmentation algorithm for color fundus images based on back-propagation (bp) neural network is proposed according to the characteristics of retinal blood vessels. Four kinds of green channel image enhancement results of adaptive histogram equalization, morphological processing, Gaussian matched filtering, and Hessian matrix filtering are used to form feature vectors. The bp neural network is input to segment blood vessels. Experiments on the color fundus image libraries DRIVE and STARE show that this algorithm can obtain complete retinal blood vessel segmentation as well as connected vessel stems and terminals. When segmenting most small blood vessels, the average accuracy on the DRIVE library reaches 0.9477, and the average accuracy on the STARE library reaches 0.9498, which has a good segmentation effect. Through verification, the algorithm is feasible and effective for blood vessel segmentation of color fundus images and can detect more capillaries.

关键词： bp neural network model Color fundus image Retinal vessel segmentation algorithm Gaussian matched filtering Hessian matrix

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Current Status and Peak Prediction of China's Carbon Emissions: An Empirical Analysis Based on bp neural network model 23

Current Status and Peak Prediction of China's Carbon Emissio...

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Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum

作者： Qian Ke Yan Wang Shuzhen Lei School of Economic and Management Xi?an University of Technology China School of Business and Circulation Shaanxi Polytechnic Institute China

ISBN: (纸本)9798400716362

Clarifying the current carbon emission situation and future carbon emissions under the existing policy basis is crucial to achieving the dual carbon goal. This paper uses the relevant data of carbon emissions from 1999∼2018, combined with the five factors of economic growth, population, industrial output value, social consumption level and energy consumption, uses linear (nonlinear) fitting to predict the data of five major variables from 2019 to 2030, and further uses the bp neural network model to predict the data of China's carbon emissions from 2019∼2030. The results show that carbon emissions in Beijing, Tianjin, Jiangsu, Zhejiang and Shanghai, Guangdong, Guangxi and other places have reached the peak state, and the western energy-oriented regions cannot achieve the carbon peak target before 2030 according to the existing carbon emission policies, and need more effective low-carbon transformation promotion and policy support.

关键词： bp neural network model Carbon Emissions Dual carbon goals Forecast

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A real-time wrist action reconstruction system design based on bp neural network model to predict multiple FES parameters

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IEEE Sensors Journal 2025年

作者： Liu, Wentao Zhang, Yuxin Zhang, Mingyu Chen, Xue Sun, Shihao Xu, Guizhi

Diseases such as stroke and spinal cord injury can cause limb movement disorders, significantly impacting daily life. To restore wrist mobility in patients and achieve precise synchronization of the affected wrist with the healthy wrist movement, this paper designs a real-time wrist motion reconstruction system using a backpropagation (bp) neural network model to predict functional electrical stimulation (FES) parameters. The system uses a multi-channel surface electromyography (sEMG) signals acquisition device to collect real-time sEMG signals from the wrist extensor and flexor muscles of the healthy forearm. The upper computer uses a sliding window strategy to capture sEMG motion frames in real-time and extract five types of time-domain features. Among these, the waveform length (WL) feature is used to identify wrist flexion and wrist extension. The five extracted time-domain features are inputted in real-time into a bp neural network model to predict the parameters of FES, including amplitude, pulse width, and frequency. The dual-channel FES device generates real-time FES pulses on the corresponding muscles of the paralyzed upper limb, achieving motion reconstruction. The experimental results show that the average correlation coefficient of the wrist joint angle curve is 0.9, with an average system delay time of 332 ms, and the average root mean square error (RMSE) of the angular velocity curve is 0.252. These results demonstrate that the system exhibits high performance in terms of real-time capability and accuracy, indicating its potential application value in wrist motion recovery. © 2001-2012 IEEE.

关键词： bp neural network model FES motion reconstruction sEMG time-domain features

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CO2 emission forecasting based on nonlinear grey Bernoulli and bp neural network combined model

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SOFT COMPUTING 2023年第21期27卷 15509-15521页

作者： Wu, Sixuan Zeng, Xiangyan Li, Chunming Cang, Haoze Tan, Qiancheng Xu, Dewei Guilin Univ Elect Technol Guangxi Coll & Univ Key Lab Data Anal & Computat Sch Math & Comp Sci Guilin 541004 Guangxi Peoples R China

Under the background of a green, low carbon economy, it is significant to accurately estimate the future CO2 emissions of countries with significant CO2 emissions for developing the world's green economy. A new Nonlinear Grey Bernoulli and bp neural network combined model (bp-ONGBM (1,1) model) have been proposed to study the CO2 emissions of China, the USA, the European Union, India and Japan. Firstly, the Particle Swarm Optimization (PSO) algorithm is optimized using the Artificial Fish Swarm Algorithm (AFSA). Then, the background value of the ONGBM (1,1) model is dynamically optimized. Based on the linearization of the model, the time response function is derived. Then, the ONGBM (1,1) model is combined with the bp neural network model. An improved PSO algorithm determines the combined weight and the background value coefficient. Finally, according to the observation data from 2010 to 2021 in the Emissions Database for Global Atmospheric Research 2022, the model is established to calculate the CO2 emissions of the selected countries from 2022 to 2026 and compared with the prediction results provided by multiple competitive models. The empirical application shows that the proposed bp-ONGBM (1,1) model is significantly better than other competitive models.

关键词： CO2 emission forecasting Nonlinear grey Bernoulli model Particle swarm optimization bp neural network model

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Prediction of Carbon Emissions Trading Price in Fujian Province: Based on bp neural network model

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Du, Yi Chen, Keren Chen, Simin Yin, Kai State Grid Fujian Elect Power Co Ltd Econ & Technol Res Inst Fuzhou Peoples R China Xiamen Univ China Ctr Energy Econ Res Sch Econ Xiamen Peoples R China

To achieve carbon peak and carbon neutrality targets, it has become a common choice for all countries to introduce the carbon emissions trading market to foster low carbon sustainable development. The construction of national carbon emissions trading market in China is still in its initial stage. However, the carbon market in Fujian province has already accumulated certain experience, and its unique energy mix of "higher share of the clean energy and low share of fossil fuels consumption" can provide guidance to China's future development. Therefore, an accurate forecast of the carbon price in Fujian province not only provides conducive suggestions for the further optimization of the carbon market in Fujian province, but also offers a significant reference for the development of China's carbon trading market. By adopting the effective daily data from 2017.01 to 2022.02, this paper predicts the carbon emissions trading price in Fujian province based on the bp neural network model and analyzes the mechanism of different influencing factors on carbon price from six dimensions. The results show that the bp neural network model works well in predicting carbon price in Fujian province and in the impact mechanism analysis. This paper also puts forward corresponding policy recommendations, which provide theoretical support for the sound development of the carbon market in Fujian province.

关键词： carbon market carbon emissions trading price mechanism analysis bp neural network model price forecast

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Research on the Investment Strategy of Private Equity Investment Fund Targeted Increase in NEEQ - An Empirical Analysis Based on bp and Hopfield neural network model

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2023年第0期

作者： Yajuan, Liu Wenbin, Xu Beijing Informat Sci & Technol Univ Sch Econ & Management Beijing 100192 Peoples R China

Private equity investment funds targeted increase in NEEQ has become a new strategy for PE investment. However, the currently adopted Logit regression and one-factor ANOVA models are not suitable for analyzing nonlinear investment activities, and the investment appraisal does not work well. In this paper, all NEEQ companies that implemented private placement in 2017 are used as the study sample. This paper also empirically analyzes the current situation of domestic private equity investment funds based on bp and Hopfield neural network models, then the results of the two models are compared. It is concluded that the accuracy of the bp neural network model can be more than 90%. So, the bp neural network can be used as the optimal model of private equity investment funds investment strategy in NEEQ.

关键词： Private equity investment fund National Equities Exchange and Quotations bp neural network model Hopfield neural network model

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