Research and application of network status prediction based on BP neural network for intelligent production line

Ma, Yue; Li, Le; Yin, Zhenyu; Chai, Anying; Li, Mingshi; Bi, Zhiying

doi:10.1016/j.procs.2021.02.049

Cited by 30 publications

(20 citation statements)

References 5 publications

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“…This strategy uses K -means clustering to establish a feature classification model based on deep convolution neural network [ 3 ]. Yin and colleagues mainly propose an intelligent production line network state prediction algorithm for the field of intelligent production, which is based on BP neural network and then improved [ 4 ]. Liu and colleagues proposed a new model of concurrent fault identification of wheelset axle box, which was based on an efficient neural network and attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.

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Section: Introductionmentioning

confidence: 99%

Machine Vision and Intelligent Algorithm Based on Neural Network

Sun

2022

Computational Intelligence and Neuroscience

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“…In particular, in the process of the gradual development of the Internet of ings, various types of local area network edge networking devices will further appear which will increase the load of network fault diagnosis [2]. (3) e development of private networks, especially some government and enterprise private Lans, is of great signi cance to their social mission.For example, the government's online government server network, a technology company's data center network, part of which uses a variety of fusion of network interaction transmission technology and network storage [3,4]. A network fault detection model based on neural network is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

2022

Mobile Information Systems

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In order to meet the new requirements of fault diagnosis response and intelligent degree in the current computer network, a fault detection of computer communication network based on an improved neural network algorithm is proposed. First, from the perspective of deep learning, based on the KDD99 data set, the network fault diagnosis method based on the convolutional neural network model is studied, and the data conversion operation of grayscale matrixed raw data is proposed. And experiments are carried out, the convolutional neural network structure is designed according to the scale of data features, a series of optimization studies including discarding learning, gradient optimization algorithm, and data enhancement based on this is carried out, and the establishment of the entire fault diagnosis model is completed. The experimental results show that, in the diagnostic model designed in this paper, the Tanh activation function is used in the first fully connected layer to achieve the best convergence speed. During the training process, it can start to converge after about 24 iterations, and the accuracy rate of the model training process can reach 98.1%, verifying the correctness and superiority of the algorithm and model.

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“…Ma and other researchers put forward a network state prediction algorithm of intelligent production line, which calculates and predicts the operation of the network through the optimized BP neural network. The results show that the optimal data prediction cycle is obtained in a large number of network data prediction experiments, and the accuracy is more than 90%, which is of great significance to improve the quality of network communication [18]. Based on the above analysis, genetic algorithm and neural network structure play an important role in classification, screening, and prediction, improve the accuracy of results, and promote the development of various fields.…”

mentioning

confidence: 95%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. The genetic algorithm is improved by immune cloning algorithm, and the CGA-BP neural network model is established to obtain the global optimal solution. Compared with the BP neural network model and GA-BP neural network model, the CGA-BP neural network model converges faster, and the training times reach the error condition after 79 times, while the BP neural network model and GA-BP neural network model need 117 times and 100 times, respectively, and the fitness value corresponding to the number of iterations of the model is larger. Therefore, the CGA-BP neural network algorithm can make prediction more accurately and quickly and predict the expansion of urban scale through urban conditions.

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Research and application of network status prediction based on BP neural network for intelligent production line

Cited by 30 publications

References 5 publications

Machine Vision and Intelligent Algorithm Based on Neural Network

Machine Vision and Intelligent Algorithm Based on Neural Network

Computer Communication Network Fault Detection Based on Improved Neural Network Algorithm

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

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