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A Quantitative Seismic Topographic Effect Prediction Method Based upon bp neural network algorithm and FEM Simulation

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Journal of Earth Science 2024年第4期35卷 1355-1366页

作者： Qifeng Jiang Mianshui Rong Wei Wei Tingting Chen Shandong Seismic Hazard Prevention Center Shandong Earthquake AgencyJinan250014China Shandong Institute of Earthquake Engineering Jinan250021China Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education Beijing University of TechnologyBeijing100124China Publicity and Education Center Shandong Earthquake AgencyJinan250014China Shandong Earthquake Station Shandong Earthquake AgencyJinan250014China

Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively *** this paper,a new quantitative seismic topographic effect prediction method based upon the bp neural network algorithm and three-dimensional finite element method(FEM)was *** FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low *** bp neural network models were established for the prediction of amplification factors of PGA and response *** kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,*** absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification *** input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive ***,the bp models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.

关键词： seismic topographic effect finite element method bp neural network algorithm earthquake disaster prevention

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Application of bp neural network algorithm in visualization system of sports training management

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SOFT COMPUTING 2023年第10期27卷 6845-6854页

作者： Zhao, Yinghui Shanxi Coll Technol Taiyuan 036000 Shanxi Peoples R China

When constructing the algorithm model of sports training action classification, the accuracy of action classification has an important impact on the algorithm model. How to improve the algorithm model to improve the accuracy of sports training action classification needs further research. Based on bp neural network algorithm, this paper carries out the modeling of bp neural network signal classification algorithm and the construction of bp neural network, and deduces the bp algorithm in detail. Firstly, this paper applies genetic algorithm to the initial parameter selection of bp algorithm to avoid the local optimization problem. When carrying out chromosome coding, binary coding is easy to cause the problem of too long coding string, which also needs to be restored and decoded. The algorithm runs too long and the learning accuracy is not high. Therefore, this paper uses real coding. Through simulation analysis, it can be seen that the classification accuracy of the improved algorithm model is significantly higher than that of the simple bp algorithm. In addition, this paper analyzes the requirements of the sports training management visualization system, introduces the system structure framework and network topology, describes in detail the functions of the user information management module, the training plan management module, the training test management module, the competition information management module and the scientific research information management module, and tests the visualization function of the system. Finally, this paper analyzes the problems existing in the current sports training management, and puts forward the development strategy of sports training management based on this, which lays a theoretical foundation for the scientific development of sports training.

关键词： bp neural network algorithm Sports training management Visualization technology System development

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A dynamic model of evaluating differential automatic method for solving plane problems based on bp neural network algorithm

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 556卷 124845-124845页

作者： He, Yuan Meng, Zhiyi Xu, Hong Zou, Yue Chengdu Univ Informat Technol Chengdu Sichuan Peoples R China State Stat Bur Key Lab Stat Informat Technol & Data Min Xicheng Peoples R China Sichuan Univ Sch Business Chengdu Sichuan Peoples R China

Aiming at solving differential equations of plane problems, the algorithm of difference equation is established, and the corresponding program is compiled on bp neural network. The correctness and practicability of the difference equation algorithm are verified. A dynamic model of the parallel difference equation is constructed according to the characteristics of the parallel structure of bp neural network. By calculating examples, the continuity condition under the condition of modulus abruption is further discussed. The study shows that the two groups of differential equations are used to identify and verify the model, and the energy function satisfies both the linear embedding condition and the correct wiring. Furthermore, bp neural network is used to realize the search and routing of the maximum plane. The results show that difference equation calculations have the ability to help bp networks get rid of local minima and get better results. (C) 2020 Published by Elsevier B.V.

关键词： bp neural network algorithm Plane problem Differential method

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Post-evaluation on financial support highway traffic project based on bp neural network algorithm

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JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2018年第4期21卷 869-879页

作者： Chen, Tiedong Xu, Weiyang Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China

Post-evaluation for road traffic project with financial support is a complex process, but the methods for the current research do not consider the non-linear complexity of such post-evaluation. This paper carries out neural network-based analysis of post-evaluation for road traffic project supported by finance. By comparing with the multiple regression method, it is suggested that bp neural network algorithm is an effective post-assessment tools in the post-evaluation of projects supported by finance with complex features, which can significantly improve the investment efficiency of highway traffic projects supported by finance.

关键词： Financial support Road traffic project bp neural network algorithm Post-evaluation

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Early Warning Method of Marine Products network Marketing Risk Based on bp neural network algorithm

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JOURNAL OF COASTAL RESEARCH 2020年第Sup1期000卷 177-181页

作者： Wang, Binggang Henan Univ Urban Construct Sch Management Pingdingshan 467036 Peoples R China

In order to reduce the early-warning error of marine products network marketing risk, bp neural network algorithm is used to optimize the early-warning method of network marketing risk. According to the structure of bp neural network, the risk early-warning model of seafood network marketing is constructed, and the risk early-warning indicators are set up under the model. This paper analyzes the mechanism of the network marketing risk. Under the risk early warning model, the bp neural network algorithm is used to train the input value of the network marketing and to judge the risk. Finally, the output results of the algorithm are compared with the set warning level to show the early warning results of seafood online marketing risks. Through the contrast experiment with the traditional early warning method, it is found that applying bp neural network algorithm to the risk early warning method of seafood network marketing can effectively reduce the error by 99.8%.

关键词： bp neural network algorithm seafood network marketing marketing risk risk early warning method

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Based on the bp neural network algorithm of Highway Engineering Cost Data Analysis 6

Based on the BP Neural Network Algorithm of Highway Engineer...

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6th International Symposium on Project Management (ISPM)

作者： Wang, Hongchuan Wu, Liping Liaoning Transportat Informat Technol Co 81 Wencui Rd Shenyang 110015 Liaoning Peoples R China Shenyang Vocat & Tech Coll Laodong Rd Shenyang 110000 Liaoning Peoples R China

ISBN: (纸本)9781921712661

Engineering information is a very precious information resources, and it is of great significance to predict the engineering cost. In this paper, using bp neural network algorithm through to complex, decentralized completed engineering data collecting and analyzing statistics and draw all kinds of traffic engineering of the quantity of consumption, and the market price of the overall trend. This can provide the basis for the investment estimation, calculation of engineering cost, engineering quotation and contract adjustment, and provide the main basis for the new engineering project decision, construction and design.

关键词： bp neural network algorithm highway engineering cost information system data analysis

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Inland Waterway 'Black Spot' Identification Model Based on MEA-bp neural network algorithm 4

Inland Waterway 'Black Spot' Identification Model Based on M...

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4th International Conference on Transportation Information and Safety (ICTIS)

作者： Dong Shi-yu Liu Qing Wang Lei Wang Haiyan Wuhan Univ Technol Sch Transportat Wuhan 430063 Hubei Peoples R China Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Hubei Peoples R China

ISBN: (纸本)9781538604373

Identification of inland waterway 'black spot' is an important means for maritime essential safety management. This paper presents an Identification model of inland waterway 'black spot' based on MEA-bp neural network algorithm. The accident information is obtained and assigned by the MEA-bp neural network algorithm. The uncertainty of navigation condition in inland waterway channel is also considered. Based on analysis of accidents in three gorges reservoir area, it shows that the proposed model can identify the inland waterway 'black spot' effectively. It will be of research significance and practical value to guarantee safety in shipping.

关键词： inland waterway black spot bp neural network algorithm mind evolutionary algorithm identification

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Application of APSO-bp neural network algorithm in Stock Price Prediction 14th

Application of APSO-BP Neural Network Algorithm in Stock Pri...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Sun, Ying He, Jingbo Gao, Yuelin North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Prov Key Lab Intelligent Informat & Data Yinchuan 750021 Ningxia Peoples R China

ISBN: (纸本)9783031366246;9783031366253

In recent years, with the rapid development of the economy, more and more people have entered the stock market for investment. Due to the volatility characteristics of the stock market, stock price prediction is often a nonlinear time series prediction. And the fluctuation of stock prices will be affected by many factors, so it is difficult to predict through a simple model. For solving this problem, a hybrid adaptive particle swarm optimization and bp neural network algorithm (APSO-bp) is proposed. The APSO-bp algorithm effectively integrates the global search ability of the PSO algorithm and the local search ability of the bp algorithm and further improves the prediction accuracy. Two sets of real stock data of China's stock market are applied to empirical analysis, and the results show that the algorithm is more effective than the standard bp algorithm in solving this problem and can provide timely risk warning information for investors.

关键词： PSO algorithm bp neural network algorithm APSO-bp algorithm Stock Price Prediction

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Application of Feature Fusion Based on DHMM Method and bp neural network algorithm in Fault Diagnosis of Gearbox 8

Application of Feature Fusion Based on DHMM Method and BP Ne...

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2017 Prognostics and System Health Management Conference (PHM-Harbin)

作者： Zhu, Wen-hui Huang, Jin-ying Feng, Shun-xiao Wei, Jie-jie Chen, Hai-xia North Univ China Sch Mech & Power Engn Taiyuan 030051 Shanxi Peoples R China North Univ China Sch Comp Sci & Control Engn Taiyuan 030051 Shanxi Peoples R China

ISBN: (纸本)9781538603703

With the development of artificial intelligence algorithm, bp neural network algorithm is widely used in many fields, such as fault diagnosis, intelligent control and dynamic signal processing, because it has many advantages for example self-learning, self-organization and nonlinear mapping. Compared with bp neural network, the hidden Markov model is suitable for dynamic time series modeling and has strong temporal classification ability. However, the hidden Markov model has problems of initial model optimization and algorithm underflow when applied to pattern classification. In this paper, the discrete hidden Markov model (DHMM) and bp neural network algorithm are combined to apply to the fault diagnosis of gearbox. Firstly, the probabilities of failures were obtained by preprocessing of the fault samples. Then the probabilities are added to the time - frequency characteristics as new features. The bp neural network algorithm were used to classify the samples whose features had been extended. The experimental results showed that the proposed method was more conducive to fault diagnosis of gearbox.

关键词： fault diagnosis bp neural network algorithm discrete hidden markov model

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Security Risk Management System for the Construction and Operation of Smart Port Area Based on bp neural network algorithm

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Procedia Computer Science 2023年 228卷 838-846页

作者： Di Cui Guoqing Sun Xiaotiao Zhan Safety and Environmental Protection Division China Waterborne Transportation Research Institute Beijing China

With the construction of smart cities, smart ports are developing rapidly, providing favorable conditions for enhancing port competitiveness, promoting industrial upgrading, and economic transformation. With the continuous progress of society and science and technology, the safety risks of intelligent ports are also increasing. In order to better address these risks, it is necessary to establish a stable and efficient management system. This paper applies data mining and fuzzy control theory technologies for large-volume, high-precision real-time information collection, based on the research of the bp neural network algorithm. By establishing and analyzing a safety risk management system model based on the bp multilayer neuron network, it provides a rationality evaluation method and design concept. After designing the safety risk management system, this paper conducts a performance test on the system. The test results show that through optimization, the accuracy, generalization ability, and reliability of the model can be improved. Its accuracy is as high as 92% or more, fully demonstrating the system's reliability and superiority!

关键词： bp neural network algorithm Smart Port Construction Port Operation Security Risk Management

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