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.
Since the idea of the supply chain management is proposed, many enterprises have attached great importance to the supply chain management and pay a lot of manpower and resources to study. It is also the focus to study...
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Since the idea of the supply chain management is proposed, many enterprises have attached great importance to the supply chain management and pay a lot of manpower and resources to study. It is also the focus to study the inventory in the field of the supply chain. Quantity of the inventory is not only related to the profit of the enterprises, but also related to the survival of the entire supply chain. Predicting the inventory can improve the ability of enterprises to prevent risk, increase the profits and reduce the losses. In order to predict better on inventory, we propose an improved bp neural network algorithm. In the algorithm, we use the improved GSA algorithm to optimize the parameters of bp neural network algorithm and improve the bp neural network algorithm aiming at its deficiency. The experimental results show that this method has good prediction effect.
bp neural network algorithm has powerful calculation ability, but the algorithm has some shortages such as low convergence which limits its application, so improving bpalgorithm has become a matter of concern in the ...
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ISBN:
(纸本)9781510812055
bp neural network algorithm has powerful calculation ability, but the algorithm has some shortages such as low convergence which limits its application, so improving bpalgorithm has become a matter of concern in the fields related. Based on analyzing improvement methods wildly used today, the paper presents a new bp neural network algorithm and applies it to evaluate food traceability system performance. Firstly, the paper improves the bpalgorithm through changing learning rate, trigonometric function to simplify the original calculation structure;secondly, the calculation step of the improved bpalgorithm is redesigned to speed up its convergence. Finally, the paper conducts the theoretical analysis of the calculation performance of the improved algorithm and applies it to evaluate food traceability system performance, the theoretical analysis and experimental evaluation results show that the improved algorithm can improve evaluation accuracy and algorithm calculation efficiency and can be used for evaluating food traceability system performance practically.
Engineering information is a very precious information resources, and it is of great significance to predict the engineering *** this paper, using bp neural network algorithm through to complex, decentralized complete...
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Engineering information is a very precious information resources, and it is of great significance to predict the engineering *** 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 *** 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.
The functional structure of the fan operating system is complex, and the condition detection of a single signal source will inevitably result in errors and false alarms. The diagnostic method of information fusion can...
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ISBN:
(数字)9781665458641
ISBN:
(纸本)9781665458641
The functional structure of the fan operating system is complex, and the condition detection of a single signal source will inevitably result in errors and false alarms. The diagnostic method of information fusion can make full use of more information. Thus the problem can be avoided: the Fan Detection robot is equipped with multiple sensors, and these sensors are fused by a reasonable information fusion algorithm. The fused sensor information can obtain a more accurate statement of the fan. The purpose of saving cost and making the fan run stably aims to reduce man-made overhauls. bpneuralnetwork has the capability of non-linear mapping, self-learning and self-adaptation, and the fusion performance is good, the application of a wide range. Therefore, bp neural network algorithm can be chosen to carry out sensor information fusion.
Since the model parameters of the shaking table exist in a non-linear form, this leads to distortion of the reproduced waveforms and can even lead to bias in the ground vibration test results. Therefore, the selection...
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Since the model parameters of the shaking table exist in a non-linear form, this leads to distortion of the reproduced waveforms and can even lead to bias in the ground vibration test results. Therefore, the selection of the controller is particularly critical. Multi-variable (MVC) controllers are often used in shaking table control, to improve the control effect of MVC controllers. In this paper, a multi-parametric (bp-MVC) controller based on bpneuralnetwork is proposed. The bpneuralnetwork is applied to the multi-parameter (MVC) controller to identify the shaking table model, adjust the parameters in real-time, accelerate the convergence speed, and reduce the system error. The simulation results show that the correlation coefficient (CC) of the bp-MVC controller is greater than 0.985, and the root-mean-square error (RMSE) and mean absolute error (MAE) are less than 0.04 and 0.25, respectively, in a nonlinear, time-varying hydraulic system. This suggests that the bp-MVC controller has a better control performance and parameter adaptivity, which can provide a reference for the subsequent ground vibration tests.
Our country's market economy is composed of enterprises. However, due to their inherent credit deficiencies and high risks of management, it is very difficult for them to obtain financing support. Based on this, t...
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Our country's market economy is composed of enterprises. However, due to their inherent credit deficiencies and high risks of management, it is very difficult for them to obtain financing support. Based on this, this article studies Error Back Propagation (bp) to establish (SMEs). Based on the relevant concepts of the supply chain management budget model, it explores the main factors influencing the financial impact of SMEs and the benefits of the supply chain budget in solving problems expenditure of SMEs, support vector machine is mainly based on solving the main credit risks of small and medium-sized enterprises, such as poor information transparency, low credit and various risk unknown factors. bpneuralnetwork is an algorithm that takes into account the components of supply chain financial financing. This article first gives a simple background and theoretical introduction to the under the current supply chain finance model, and then proposes to use SVM and bp neural network algorithms to build and the model has been trained and tested. After collecting relevant references, we will establish authoritative risk assessment rules in accordance with this article according to these standards. These experts are mainly people with many years of experience in the financial industry, and they also have a certain influence in the industry. The risk assessment established for this can be the analysis of factors such as risk indicators and government intervention in this experiment.
In order to realize the real-time diagnosis of local insulator pollution, a kind of local insulator pollution diagnosis device is designed based on bp (Back Propagation) neuralnetwork prediction and classification al...
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ISBN:
(纸本)9781665425513
In order to realize the real-time diagnosis of local insulator pollution, a kind of local insulator pollution diagnosis device is designed based on bp (Back Propagation) neuralnetwork prediction and classification algorithm, which can communicate with domestic ultraviolet imager in real time. According to the obtained photoelectron number, apparent discharge, detection distance and gain from ultraviolet imager, the insulator pollution grade is evaluated by bp neural network algorithm. The research results lay a foundation for the practical engineering application of the local insulator pollution diagnosis device.
Due to the hydration of cement is especially obvious in mass concrete, it is challenging to accurately predict the temperature field and prevent the formation of temperature cracks. In this study, the parameters of th...
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Due to the hydration of cement is especially obvious in mass concrete, it is challenging to accurately predict the temperature field and prevent the formation of temperature cracks. In this study, the parameters of the kinetic model of cement hydration applicable to this composite cementitious system were first obtained by the back propagation (bp) neuralnetworkalgorithm, and then the three-dimensional temperature field time evolution under different heat source functions was obtained from the kinetic model of cement hydration, isothermal and adiabatic conditions of cumulative hydration exotherm by developing a finite element calculation model for the mass slab concrete, respectively. The calculation results show that the kinetic model of cement hydration as a new heat source function is suitable for the calculation of the temperature field of mass concrete, and the hydration heat of isothermal calorimetry yields low results. In addition, in the temperature field calculation of mass concrete, it is more accurate to consider the effect of temperature on cement hydration than not to consider temperature.
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