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 u...
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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 *** bpneuralnetwork 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.
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 a...
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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 bpneuralnetwork signal classification algorithm and the construction of bpneuralnetwork, and deduces the bpalgorithm in detail. Firstly, this paper applies genetic algorithm to the initial parameter selection of bpalgorithm 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 bpalgorithm. 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.
Aiming at solving differential equations of plane problems, the algorithm of difference equation is established, and the corresponding program is compiled on bpneuralnetwork. The correctness and practicability of th...
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Aiming at solving differential equations of plane problems, the algorithm of difference equation is established, and the corresponding program is compiled on bpneuralnetwork. 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 bpneuralnetwork. 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, bpneuralnetwork is used to realize the search and routing of the maximum plane. The results show that difference equation calculations have the ability to help bpnetworks get rid of local minima and get better results. (C) 2020 Published by Elsevier B.V.
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 B...
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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 bpneuralnetwork, 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%.
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 com...
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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.
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...
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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-bpalgorithm effectively integrates the global search ability of the PSO algorithm and the local search ability of the bpalgorithm 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 bpalgorithm in solving this problem and can provide timely risk warning information for investors.
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-B...
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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.
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 adv...
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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 bpneuralnetwork, 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.
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 conti...
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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!
Highway construction project does not exist for its own, but also to meet the needs of the society. Its development strategy should be based on the overall goal of the society, not just for its own. Therefore, the sus...
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Highway construction project does not exist for its own, but also to meet the needs of the society. Its development strategy should be based on the overall goal of the society, not just for its own. Therefore, the sustainable evaluation of the highway construction should be considered from the two parts of sustainability of social needs and economic development. In this paper, using the bp neural network algorithm, through the analysis of sustainable development of the following four areas in road construction: economics, environmental resources, operations, management systems and policy, the author studies the sustainable development evaluation of highway construction project.
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